Integrated sensing and communications (ISAC) is a disruptive technology enabling future sixth-generation (6G) networks. This paper investigates target detection in a bistatic ISAC system, in which the base station (BS) transmits superimposed ISAC signals comprising both Gaussian information-bearing and deterministic sensing components to simultaneously provide communication and sensing functionalities. First, we develop a Neyman-Pearson (NP)-based detector that effectively utilizes both the deterministic sensing and random communication signals. Closed-form analysis reveals that both signal components contribute to improving the overall detection performance. Subsequently, we optimize the BS transmit beamforming to maximize the detection probability, subject to a minimum signal-to-interference-plus-noise ratio (SINR) constraint for the communication user (CU) and a total transmit power budget at the BS. The resulting non-convex beamforming optimization problem is addressed via semi-definite relaxation (SDR) and successive convex approximation (SCA) techniques. Simulation results demonstrate the superiority of the proposed NP-based detector, which leverages both types of signals, over benchmark schemes that treat information signals as interference. They also reveal that a higher communication-rate threshold directs more transmit power to Gaussian information-bearing signals, thereby diminishing deterministic-signal power and weakening detection performance.

The computational complexity of multiple-input multiple-output (MIMO) signal processing algorithms grows exponentially, resulting in increasing computational latency. Conventional approaches to reducing latency are predominantly algorithm-specific, addressing only limited components within the overall MIMO system. In this paper, we propose a novel arithmetic-level variable precision computing (VPC) scheme, introducing a new universal compatible methodology for computational latency reduction. The proposed arithmetic-level VPC dynamically assigns varying levels of computing precision to individual arithmetic operations within an algorithm and employs a Markovian process model with low-complexity computations to minimize additional complexity overhead introduced by VPC. Numerical simulations demonstrate that the proposed scheme significantly outperforms conventional fixed-length computing (FLC), achieving superior performance while maintaining the same level of average computing precision.

Energy efficiency is of paramount importance for covert integrated sensing and communication (ISAC) networks to ensure sustained operation. In light of the imperfect channel state information (CSI) encountered in practical scenarios, we investigate the energy efficiency of these networks. Taking into account a variety of CSI estimation errors, our algorithm optimizes both sensing and information beamforming design while ensuring a low detection probability by multiple untrusted wardens. The energy-efficient beamforming design is formulated as a non-convex fractional programming problem. First, we establish that the covariance matrices of communication beamforming vectors are rank-one. Subsequently, we exploit this property to transform the original problem into a semi-definite relaxed version. For Gaussian CSI estimation errors, we adopt Bernstein-type inequalities to handle the probability constraints of interception and exploit Dinkelbach’s algorithm to address the nonlinear fractional objective function. For bounded CSI estimation errors, we employ an S-procedure to tackle the non-convex constraints associated with covert communications, followed by a successive convex optimization algorithm to provide an effective solution to the original problem. Extensive simulations confirm the superiority of our proposed algorithms, demonstrating a remarkable performance gain compared with baseline schemes adopting existing approaches. Specifically, deploying a larger number of antenna elements can enhance the energy efficiency of covert ISAC networks, while simultaneously reducing the system’s total power consumption. Furthermore, the sensing beam power threshold and the outage probability of covertness serve as important trade-off parameters in covert ISAC networks.

This paper investigates physical-layer key generation (PLKG) in multi-antenna base station systems, by leveraging a fluid antenna system (FAS) to dynamically customize radio environments. Without requiring additional nodes or extensive radio frequency (RF) chains, the FAS effectively enables adaptive antenna port selection by exploiting channel spatial correlation to enhance the secret key rate (SKR) at legitimate nodes. To comprehensively evaluate the performance of the FAS in PLKG, we propose an FAS-assisted PLKG model that integrates transmit beamforming and sparse port selection under independent and identically distributed (i.i.d.) and spatially correlated channel models, respectively. Specifically, the PLKG utilizes reciprocal channel probing to derive an approximate SKR expression based on the mutual information between legitimate channel estimates, explicitly accounting for the Eve’s channel observation under spatially correlated channel scenarios. Nonconvex optimization problems for these scenarios are formulated to maximize the SKR subject to transmit power constraints and sparse port activation. We propose an iterative algorithm by capitalizing on successive convex approximation and Cauchy-Schwarz inequality to obtain a locally optimal solution. A reweighted ℓ1-norm-based algorithm is applied to advocate for the sparse port activation of FAS-assisted PLKG. To approximate the optimal activated ports obtained by exhaustive search, a low-complexity sliding window-based port selection is proposed to substitute reweighted ℓ1-norm method based on Rayleigh-quotient analysis. Simulation results demonstrate that the FAS-assisted PLKG scheme significantly outperforms fixed antenna-assisted PLKG schemes in both environments. It is shown that the FAS achieves higher SKR with fewer RF chains through dynamic sparse port selection, which effectively reduces the resource overhead. Also, the sliding window approach closely approximates the globally optimal port selection compared to the reweighted ℓ1-norm method, rendering it suitable for practical deployments.

In this paper, we investigate the problem of predictive beamforming design for tracking vehicles in an integrated sensing and communication (ISAC)-based near-field vehicle-toinfrastructure (V2I) system. The waveform design in near-field scenarios requires the joint consideration of both range and angle dimensions, posing new challenges to conventional beamforming and tracking strategies. To address this issue, we propose a predictive beamforming framework leveraging a large language model (LLM)-based neural network (LNN), which exploits historical channel state information (CSI) to facilitate accurate future beamforming decisions. Cramér–Rao bounds (CRBs) for angle and distance estimation, along with the achievable sum-rate, are applied as key metrics to evaluate the sensing and communication performance of the V2I system, respectively. Capitalizing on the derived performance metrics, we formulate the optimization problems aiming either to maximize the sum-rate subject to CRB constraints or to minimize the CRB while ensuring a required communication rate, thereby accommodating different design requirements. Moreover, to effectively capture the stochastic nature of vehicle driving behavior, the performance metrics are further expressed in expectation form over the distribution of possible driving states. Consequently, a data-driven optimization approach based on the LNN is adopted to handle the resulting intractable analytical expressions, and the underlying LNN is trained with task-specific loss functions. During the training process, low-rank adaptation (LoRA) is incorporated to fine-tune the pre-trained LLM, which significantly reduces the number of trainable parameters. Simulation results demonstrate that the proposed framework accurately predicts future vehicle kinematic parameters and effectively optimizes the power allocation across transmit links. As a result, it achieves superior and robust performance in both communication and sensing tasks, highlighting its potential as a vital solution for next-generation near-field V2I systems.

This paper investigates the joint transmit and receive beamforming design for a bistatic integrated sensing and communication (ISAC) system, where a transmit base station (BS) and a receive BS are coordinated to simultaneously serve multiple downlink and uplink users as well as estimate target positions. The closed-form expression for the Cramér-Rao bound (CRB) for target positions and reflection coefficients is derived and minimized subject to constraints of the transmit power budget and communication requirements. To address the considered problem, the closed-form expression for the optimal receive beamforming vectors is derived, facilitating the development of a successive convex approximation (SCA)-based algorithm for optimizing the transmit information beamforming vectors and sensing covariance matrix. In addition, a learning-based approach named ISACNet, trained in an unsupervised manner, is proposed to handle the considered problem. The ISACNet incorporates multi-head self-attention and cross-attention mechanisms to significantly enhance its expressive capability. Simulations validate the effectiveness of the proposed SCA-based algorithm and ISACNet. It is observed that the sensing performance is predominantly affected by the downlink communication more than the uplink communication. Furthermore, our ISACNet generates an effective solution in millisecond-level response times with only a marginal performance degradation compared to the SCA-based algorithm.

In decentralized federated learning (FL), multiple clients collaboratively learn a shared machine learning (ML) model by leveraging their privately held datasets distributed across the network, through interactive exchange of intermediate model updates. To ensure data security, cryptographic techniques are commonly employed to protect model updates during aggregation. Despite growing interest in secure aggregation, existing works predominantly focus on protocol design and computational guarantees, with limited understanding of the fundamental information-theoretic limits of such systems. Moreover, optimal bounds on communication and key usage remain unknown in decentralized settings, where no central aggregator is available. Motivated by these gaps, we study the problem of decentralized secure aggregation (DSA) from an information-theoretic perspective. Specifically, we consider a network ofKfully-connected users, each holding a private input—an abstraction of local training data—who aim to securely compute the sum of all inputs. The security constraint requires that no user learns anything beyond the input sum, even when colluding with up toTother users. We characterize the optimal rate region, which specifies the minimum achievable communication and secret key rates for DSA. In particular, we show that to securely compute one symbol of the desired input sum, each user must (i) transmit at least one symbol to others, (ii) hold at least one symbol of secret key, and (iii) all users must collectively hold no fewer thanK−1independent key symbols. Our results establish the fundamental performance limits of DSA, providing insights for the design of provably secure and communication-efficient protocols in decentralized learning.

The reconfigurability of fluid antenna systems (FASs) and reconfigurable intelligent surfaces (RISs) provides significant flexibility in optimizing channel conditions by jointly adjusting the positions of fluid antennas and the phase shifts of RISs. However, it is challenging to acquire the instantaneous channel state information (CSI) for both fluid antennas and RISs, while frequent adjustment of antenna positions and phase shifts will significantly increase the system complexity. To tackle this issue, this paper investigates the two-timescale design for FAS-RIS multi-user systems with linear precoding, where only the linear precoder design requires instantaneous CSI of the end-to-end channel, while the FAS and RIS optimization relies on statistical CSI. The main challenge comes from the complex structure of channel and inverse operations in linear precoding, such as regularized zero-forcing (RZF) and zero-forcing (ZF). Leveraging on random matrix theory (RMT), we first investigate the fundamental limits of FAS-RIS systems with RZF/ZF precoding by deriving the ergodic sum rate (ESR). This result is utilized to determine the minimum number of selected antennas to achieve a given ESR. Based on the evaluation result, we propose an algorithm to jointly optimize the antenna selection, regularization factor of RZF, and phase shifts at the RIS. Numerical results validate the accuracy of performance evaluation and demonstrate that the performance gain brought by joint FAS and RIS design is more pronounced with a larger number of users.

In this paper, we investigate the channel estimation problem in near-field extremely large-scale multiple-input multiple-output (XL-MIMO) systems, explicitly accounting for both spherical-wave propagation characteristics and spatial non-stationary effects. Building on these properties, we propose a novel model-based deep learning framework that delivers high-accuracy channel estimation with low computational complexity by tightly integrating domain knowledge and data-driven learning. Specifically, the proposed framework comprises three key unfolding networks: a sparse channel recovery network, a codebook update network, and an error cancellation network. The first network, referred to as variational Bayesian inference (VBI)-Net, is derived by unfolding the inverse-free VBI (IF-VBI) algorithm. It enables high-precision sparse channel reconstruction without requiring explicit prior assumptions, by learning the underlying precision distribution directly from data. The second network, gradient (Grad)-Net, is developed by unfolding the gradient ascent procedure, where learnable step sizes are introduced to adaptively refine the parameters of the polar-domain grids. Moreover, Grad-Net captures spatial non-stationary characteristics associated with the polar-domain representation by jointly exploiting gradient information and estimated path parameters. The third network, termed projected gradient descent (PGD)-Net, is constructed by unfolding the PGD algorithm. It iteratively refines the channel estimates and effectively suppresses residual estimation errors induced by spherical-wave propagation and spatial non-stationarity. Extensive numerical simulations demonstrate that the proposed framework significantly outperforms existing methods in both estimation accuracy and computational efficiency. Furthermore, the proposed framework achieves a superior accuracy-complexity tradeoff for practical XL-MIMO systems, delivering enhanced performance while maintaining very low computational complexity.

In frequency division duplex (FDD) massive multiple-input multiple-output (mMIMO) systems, the uplink and downlink channel state information (CSI) exhibits an implicit reciprocal relationship, which can be leveraged for effective CSI compression and feedback. However, directly learning the reciprocity from complex-valued CSI matrices is prone to introducing irrelevant information or noise into the reconstructed downlink CSI, due to the high sensitivity of real and imaginary CSI components to frequency variations across the uplink and downlink. As the CSI magnitude is influenced by the propagation path common to both uplink and downlink, it demonstrates a strong degree of reciprocity than the real and imaginary parts of the CSI. To efficiently extract the reciprocal information, we propose a magnitude and phase separated CSI feedback neural network, named MPSCsiNet. Specifically, the proposed MPSCsiNet adopts a disentangled representation learning (DRL) network to accurately capture the reciprocal relationship between uplink and downlink CSI magnitudes, while applying a filtering approach to compress the essential information in the downlink CSI phases. Numerical results demonstrate that compared with the existing state-of-the-art AI compression feedback methods, MPSCsiNet can reduce the computational complexity by more than 80% while improving the accuracy of CSI recovery by approximately 4 dB, which verifies the advantages of integrating domain knowledge into deep learning (DL).

Integrated sensing and communication (ISAC) emerges as a transformative paradigm for enabling future wireless networks by jointly supporting high-rate communication and precise environmental perception. In this paper, we investigate a robust beamforming framework tailored for monostatic ISAC systems in near-field channels, where the impact of spherical wavefront effects is significant and cannot be neglected. In particular, we formulate a minimum mean squared error (MMSE)-driven beamforming optimization problem that strikes an effective balance between sensing accuracy and communication quality, while considering both power and outage constraints and explicitly accounting for channel estimation errors. To address the inherent non-convexity arising from coupled sensing-communication constraints and uncertainties due to channel errors, a semidefinite relaxation (SDR)-based algorithm leveraging sphere bounding techniques is developed to acquire a favorable suboptimal solution, with polynomial-time complexity. Furthermore, extensive simulations are conducted to rigorously validate the proposed methodology under a variety of practical conditions. Our results demonstrate that the proposed design reduces sensing mean squared error (MSE) by up to 3.8 dB and improves achievable communication rate by 33.4% over non-robust baselines under imperfect channel state information (CSI). Furthermore, compared to conventional far-field schemes, our design achieves up to 1.6 dB lower MSE and 28.0% higher rate in millimeter-wave (mmWave) scenarios, and 2.5 dB lower MSE and 31.7% rate gain in terahertz (THz) scenarios at 10 dB communication signal-to-interference-plus-noise ratio (SINR). To further underscore the practical significance of our approach, the experimental results reveal critical insights into the delicate balance between communication and sensing performance in ISAC systems under real-world conditions. Specifically, our findings emphasize the importance of robust beamforming techniques that optimize resource allocation to mitigate the adverse effects of channel estimation errors, particularly in high-frequency regimes such as mmWave and THz. These insights are vital for the design of adaptive ISAC systems, where trade-offs between sensing precision and communication reliability must be dynamically managed to meet stringent performance requirements in next-generation wireless networks.

Networked integrated sensing and communication (ISAC) has emerged as a pivotal paradigm for next-generation wireless networks, where dedicated target monitoring terminals (TMTs) can be extensively leveraged for their low-cost flexible deployment and capability to facilitate bistatic and multistatic sensing. Nevertheless, the coordinated beamforming design for networked ISAC tailored for time-of-arrival (ToA)-based multi-TMT localization remains largely unexplored. To address this gap, we present a comprehensive study in this paper. Specifically, we first establish signal models for both communication and localization, and, for the first time, derive a closed-form Cramér-Rao lower bound (CRLB) to quantify the localization performance. Leveraging this CRLB, we formulate two optimization problems focusing on sensing-centric and communication-centric criteria, respectively, to thoroughly investigate the fundamental communication-localization trade-offs. For the sensing-centric problem, we develop a globally optimal algorithm based on semidefinite relaxation (SDR), applicable to scenarios where the number of BS antennas exceeds the total number of communication users. In parallel, for the communication-centric problem, we design a globally optimal algorithm for the single-BS case utilizing bisection search. To address the general cases of both problems, we propose a unified and efficient successive convex approximation (SCA)-based algorithm, which is further extended to multi-target scenarios. Finally, simulation results demonstrate the effectiveness of our proposed algorithms, reveal the intrinsic trade-offs between communication and localization, and further show that deploying more TMTs is more beneficial than deploying more BSs in networked ISAC systems.

This paper investigates transceiver design optimization strategies for frequency diverse array (FDA)-multiple-input multiple-output (MIMO) radar-communication coexistence (RCC) systems, focusing on both radar-centric and communication-centric modes. Specifically, the former formulates the design problem to maximize the signal-to-interference-plus-noise ratio (SINR) in mainlobe deceptive jammer scenarios, whereas the latter aims to maximize the communication rate while simultaneously satisfying a predefined radar SINR constraint. In this framework, practical constraints pertaining to the radar’s transmitted waveform, communication codebook, frequency increment, and receive filter are taken into account. To address the resultant non-convex and NP-hard optimization problems, a maximum block improvement (MBI) approach is employed, where the variables are alternately examined, which are achieved either by leveraging closed-form expressions and hidden convexities or by resorting to the minorization-maximization (MM) approach, while keeping the remaining parameters fixed. The convergence performance of the devised algorithm is thoroughly examined, alongside their computational complexity analyses. Numerical results are provided to validate the efficacy of our approach against mainlobe deceptive jammers, demonstrating superior SINR and communication rate performance compared to existing optimization strategies and benchmark system frameworks.

The rapid evolution of the forthcoming sixth-generation (6G) wireless network necessitates seamless integration of artificial intelligence (AI) with wireless communications to support emerging intelligent applications that demand both efficient communication and robust learning performance. This dual requirement calls for a unified framework of integrated learning and communication (ILAC), where AI enhances communication through intelligent signal processing and resource management, while wireless networks facilitate AI model deployment by enabling efficient and reliable data exchanges. However, achieving this integration presents significant challenges in practice. Communication constraints, such as limited bandwidth and fluctuating channels, hinder learning accuracy and convergence. Simultaneously, AI-driven learning dynamics, including model updates and task-driven inference, introduce excessive burdens on communication, necessitating flexible context-aware transmission strategies. This paper provides a comprehensive overview of ILAC design and optimization strategies. We establish corresponding foundational principles, covering system architectures and presenting a unified optimization formulation that closely links learning performance with communication efficiency. We then review recent advancements in ILAC from the strategic perspectives of model and data distributions, computational complexity, and communication overhead. Despite considerable progress, existing ILAC approaches still suffer from high communication overhead, unstable convergence, and scalability challenges. To address these issues, we propose an enhanced ILAC framework with large AI models leveraging hyperdimensional computing (HDC). In particular, utilizing large AI models improves generalization capabilities under dynamic task and network conditions, while HDC provides lightweight high-dimensional representations that reduce both communication and learning costs. Finally, we present a case study on a cost-to-performance optimization problem, where task assignments, model size selection, bandwidth allocation, and transmission power control are jointly optimized, aiming at improving both communication efficiency and inference accuracy with reduced computational cost. Leveraging the Dinkelbach and alternating optimization algorithms, we offer a practical and effective solution to achieve an optimal balance between learning performance and communication constraints.

This paper investigates energy-minimal data collection from multiple low-power ground devices (GDs) adopting a rotary-wing unmanned aerial vehicle (UAV). Unlike most prior works that assume flexible trajectories, the UAV in our study adheres to a fixed or predetermined flight path, due to practical requirements from e.g. patrol and inspection missions. To improve communication performance and prolong GDs’ operational lifetime, the UAV employs a directional antenna for wirelessly transferring energy to the GDs before collecting their data. We jointly optimize the UAV’s flight speeds, hovering locations, and radio resource allocation along the predefined flight path to minimize the UAV’s total energy consumption. For acyclic flight paths, we show that the UAV’s propulsion energy consumption is a strictly convex function of flight speed. However, the fixed path imposes a stringent nonconvex constraint, complicating the optimization. To overcome this challenge, we decompose the problem into two layers and solve it by proposing a novel monotonic optimization method in polar coordinates, referred to aspolar polyblock approximation. This method guarantees a globally optimal solution under mild conditions. Additionally, we propose a low-complexity suboptimal algorithm to balance system performance and computational efficiency. Simulation results show that both the proposed optimal and suboptimal algorithms can effectively mitigate the limitation of a fixed flight path, resulting in significant reductions in the UAV’s energy consumption during data collection, with more energy savings achieved as antenna directivity increases.

This paper proposes a joint beamforming design for optimizing integrated sensing and communication (ISAC) systems under imperfect channel state information (CSI), leveraging a simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS). Owing to the deployment of large-scale antenna arrays and high carrier frequencies, the Rayleigh distance can extend to tens or even hundreds of meters. This expansion leads to a fundamental paradigm shift in electromagnetic field characteristics, transitioning from the conventional far-field regime to the emerging near-field regime. As a result, the propagation characteristics experienced by users and the target may differ. Accordingly, we consider a practical scenario in which users and the target are situated in distinct fields. However, this hybrid-field model increases the system’s sensitivity to channel estimation errors (CEE). To this end, we propose a robust design that jointly optimizes beamforming for base station (BS) and STAR-RIS, aiming to maximize the achievable sum-rate of the nodes while satisfying the constraint of sensing requirements. Under a statistical CEE model, we derive the interference covariance matrix and reformulate the maximization problem as an equivalent weighted mean square error (MSE) minimization problem. Subsequently, the transformed problem is decoupled into multiple sub-problems using a block coordinate descent (BCD)-based algorithm. The algorithm capitalizes on semidefinite relaxation and Gaussian randomization to obtain an effective solution. Finally, simulation results validate the effectiveness of the proposed robust design. Compared with the baseline schemes, the proposed algorithm achieves a higher communication sum-rate and illustrates how various parameters affect the performance.

The Internet-of-Things (IoT) enables the connection of myriad wireless devices, generating a massive influx of data that can overwhelm central servers. Federated learning (FL) mitigates these issues by distributing computation tasks across edge devices, thus preserving data privacy by eliminating the need for raw data transmission. However, when deployed in large-scale IoT networks, FL encounters several challenges such as device energy constraints, heterogeneous network conditions, and the straggler effect, which inevitably impedes model convergence. In response, this paper proposes a semi-decentralized hybrid FL (SDHFL) scheme leveraging an unmanned aerial vehicle (UAV) as a mobile data center to collect data from distributed IoT clusters. To further expedite FL convergence, we formulate a non-convex mixed-integer nonlinear programming (MINLP) problem to minimize overall completion time while ensuring quality of service (QoS) requirements, considering both the UAV's energy constraints and network stability. We demonstrate that efficient learning is achieved by optimizing both power allocation and device computational capability. Furthermore, we prove the convergence of the proposed SDHFL scheme and derive the minimum number of global iterations required. Exploiting these insights, we introduce a low-complexity suboptimal algorithm for dynamic cluster selection and resource allocation optimization, leveraging Lyapunov optimization theory to obtain optimal solutions efficiently, demonstrating its scalability for large-scale networks. Our simulation results validate the effectiveness of the proposed SDHFL framework, revealing a non-trivial tradeoff where the overall completion time initially decreases and then increases as the number of devices or clusters grows, indicating the necessity of optimizing both cluster counts and intra-cluster device allocations. Furthermore, compared to several baseline schemes, our proposed optimal algorithms significantly reduce overall completion time and enhance model convergence, demonstrating the potential of SDHFL for enabling efficient and scalable FL in IoT networks.

This paper studies a novel movable antenna (MA)-enhanced multiuser multiple-input multiple-output downlink system designed to improve wireless communication performance. We aim to maximize the average achievable sum rate through two-timescale optimization exploiting instantaneous channel state information at the receiver (I-CSIR) for receive antenna position vector (APV) design and statistical channel state information at the transmitter (S-CSIT) for transmit APV and covariance matrix design. We first decompose the resulting stochastic optimization problem into a series of short-term problems and one long-term problem. Then, a gradient ascent algorithm is proposed to obtain suboptimal receive APVs for the short-term problems for given I-CSIR samples. Based on the output of the gradient ascent algorithm, a series of convex objective/feasibility surrogates for the long-term problem are constructed and solved utilizing the constrained stochastic successive convex approximation (CSSCA) algorithm. Furthermore, we propose a planar movement mode for the receive MAs to facilitate efficient antenna movement and the development of a low-complexity primal-dual decomposition-based stochastic successive convex approximation (PDD-SSCA) algorithm, which finds Karush-Kuhn-Tucker (KKT) solutions almost surely. Our numerical results reveal that, for both the general and the planar movement modes, the proposed two-timescale MA-enhanced system design significantly improves the average achievable sum rate and the feasibility of the formulated problem compared to benchmark schemes.

Cell-free (CF) massive multiple-input multiple-output (MIMO) has recently emerged as a highly promising technology for supporting future six-generation (6G) networks, owing to its unique ability to provide high data rates and reliable connectivity. However, a primary challenge in CF massive MIMO is severe inter-user interference, which is caused by densely located user equipments (UEs) and the presence of imperfect channel state information (CSI). Fortunately, the rate-splitting (RS) strategy offers significant benefits by enabling partially interference decoding, thereby greatly enhancing overall system performance. In this paper, we investigate the performance of uplink RS in CF massive MIMO systems. Considering the inevitable channel estimation errors caused by pilot contamination, we first derive a novel closed-form expression for characterizing spectral efficiency (SE). Moreover, we propose two innovative decoding strategies tailored to the 6G scenario, highlighting their role in enhancing the interference management capabilities of RS, while balancing decoding performance with computational complexity. To ensure successful decoding of each sub-message to the greatest extent possible, we devise an optimization-based power control scheme to maximize the minimum SE of the sub-messages, and propose a low-complexity scheme for comparative analysis. Additionally, we investigate the total energy efficiency (EE) of the system and propose a power control scheme for maximizing EE by exploiting fractional programming (FP) theory. Simulation results corroborate our theoretical expressions and demonstrate that both RS and the proposed power control schemes can significantly improve both SE and EE.

Cell-free (CF) massive multiple-input multiple-output (MIMO) has emerged as a promising technique to deliver uniform signal coverage and high data rates. However, employing low-precision hardware in user equipment introduces susceptibility to hardware impairments (HI), resulting in significantly degraded channel state information (CSI) accuracy. Fortunately, rate-splitting multiple access (RSMA) has been proposed as a robust solution to mitigate the adverse effects of imperfect CSI by performing message splitting at the transmitter and successive interference cancellation (SIC) at the receiver. In this paper, we incorporate RSMA into CF massive MIMO systems to tackle the problem posed by imperfect CSI. Taking into account inevitable pilot contamination, we first derive a novel and closed-form expression for the spectral efficiency (SE) to analytically characterize the performance of RSMA-enabled CF massive MIMO systems under spatially correlated Rician fading channels. Subsequently, we focus on optimizing the decoding order, power allocation, and fronthaul weights to maximize the system’s sum SE. To address this mixed-integer nonlinear programming (MINLP) problem, we initially propose an alternating optimization (AO)-based optimization method that decomposes the original intractable problem into three manageable subproblems, which are iteratively handled until convergence. Considering the significant computational complexity associated with the AO-based approach, we further propose a proximal policy optimization (PPO)-based method to establish an effective and low-complexity optimization framework. Simulation results unveil the detrimental impact of HI on both CSI accuracy and the overall sum SE performance. In particular, the presence of HI introduces residual interference that limits the performance gains achievable through additional RSMA layers, especially in strong line-of-sight scenarios, highlighting the trade-off between these gains and the SIC-related costs in terms of computational complexity and decoding latency.

Sensing-assisted predictive beamforming shows significant promise for enhancing various future unmanned aerial vehicle (UAV) applications in integrated sensing and communication (ISAC) systems. However, the impact of such beamforming technique on the communication reliability was largely unexplored and challenging to characterize. To fill this research gap and tackle this issue, this paper proposes a cellular-connected UAV tracking scheme leveraging extended Kalman filtering (EKF), where the predicted UAV trajectory, sensing duration ratio, and target constant received signal-to-noise ratio (SNR) are jointly optimized to maximize the outage capacity at each time slot. To address the implicit nature of the objective function, analytical outage probability (OP) approximations are proposed based on second-order Taylor expansions, providing an efficient and full characterization of outage capacity. Subsequently, an efficient algorithm is proposed based on a combination of bisection search and successive convex approximation (SCA) to address the non-convex optimization problem with guaranteed convergence. To further reduce computational complexity, a second efficient algorithm is developed based on alternating optimization (AO). Simulation results validate the accuracy of the derived OP approximations, the effectiveness of the proposed algorithms, and the significant outage capacity enhancement over various benchmarks. Furthermore, we show that the optimized predicted UAV trajectory tends to be parallel to the base station’s uniform linear array antennas with a nonzero minimum distance, indicating a trade-off between decreasing path loss and enjoying wide beam coverage for outage capacity maximization.

As a novel category of beyond-diagonal (BD)-reconfigurable intelligent surfaces (RISs), the fully-connected (FC)-RIS represents an unprecedented advancement in RIS technology for wireless networks. To unlock the full potential of FC-RISs for physical-layer surveillance, this paper investigates a wireless surveillance system assisted by an aerial FC-RIS. Specifically, a legitimate monitoring station exploits an unmanned aerial vehicle (UAV)-borne FC-RIS to enhance the channels related to the monitoring station for facilitating its monitoring of a signal transmitted from a suspicious source. Meanwhile, this signal is decoded at the suspicious destination. Capitalizing on the unparalleled configuration flexibility of FC-RISs, we reduce the implementation complexity associated with jointly optimizing both antenna selection and RIS configuration. In particular, we opportunistically select a antenna at the legitimate multi-antenna monitoring station for signal reception without requiring iterative RIS optimization. Accordingly, several schemes are proposed, each differentiated by their specific selection criteria: 1) round-robin antenna selection and FC-RIS-aided (RAS-FR), 2) antenna selection combined with known FC-RIS reflecting channels (ASC-FRRC), 3) threshold-based antenna selection with known FC-RIS reflecting channels (TAS-FRRC). Considering that successful monitoring can be achieved when the monitoring channel conditions surpass those of the suspicious channels, we derive closed-form expressions for monitoring success probabilities (MSPs) of the RAS-FR, ASC-FRRC, and TAS-FRRC schemes, respectively. Furthermore, when part of small-scale channel state information (CSI) is unavailable due to the inherent non-cooperative nature of suspicious party, the surveillance performance can be further improved by optimizing the UAV-borne FC-RIS location. Numerical results not only validate our closed-form MSP analysis, but also verify that the considered UAV-borne FC-RIS aided surveillance system outperforms the conventional diagonal-RIS or terrestrial-RIS-assisted surveillance systems in terms of MSP. Moreover, compared with non-channel-aware schemes or antenna selection with CSI of all sub-links in cascaded links, the proposed AS-FRRC framework dramatically reduces the computational complexity for selection and RIS optimization without introducing performance loss. Additionally, the TAS-FRRC scheme can achieve a more favourable performance-complexity tradeoff than the RAS-FR and ASC-FRRC schemes.

Extremely large-scale multiple-input multiple-output (XL-MIMO) systems are pivotal to next-generation wireless communications, where dynamic RF chain architectures offer enhanced performance. However, efficient precoding in such systems requires accurate channel state information (CSI) obtained with low complexity. To address this challenge, spatial-domain channel extrapolation has attracted growing interest. Existing methods often overlook near-field spherical wavefronts or rely heavily on sparsity priors, leading to performance degradation. In this paper, we propose an adaptive near-field channel extrapolation framework for multi-subcarrier XL-MIMO systems, leveraging a strategically selected subset of antennas. Subsequently, we develop both on-grid and off-grid algorithms, where the latter refines the former’s estimates for improved accuracy. To further reduce complexity, a cross-validation (CV)-based scheme is introduced. Additionally, we analytically formulate the mutual coherence of the sensing matrix and propose a coherence-minimizing-based random pattern to ensure robust extrapolation. Numerical results validate that the proposed algorithms significantly outperform existing methods in both extrapolation accuracy and achievable rate, while maintaining low computational complexity. In particular, our proposed CV ratio offers a flexible trade-off between accuracy and efficiency, and the corresponding off-grid algorithm achieves high accuracy with complexity comparable to conventional on-grid methods.

The integration of reconfigurable intelligent surfaces (RIS) with cell-free massive MIMO (CF mMIMO) represents a compelling paradigm for satisfying the stringent reliability and latency demands of ultra-reliable low-latency communication (URLLC). In this framework, distributed access points (APs) provide substantial macro-diversity gains, while dynamically controllable RIS elements facilitate enhanced signal propagation. This paper investigates a practical RIS-aided CF mMIMO system designed for URLLC applications, where communications occur through RIS-reflected links under realistic spatially correlated Rayleigh fading channels, with practical impairments such as RIS phase estimation errors and electromagnetic interference explicitly considered. To evaluate reliability in the short-packet regime, we adopt the decoding error probability (DEP) as the performance metric and derive its analytical expression based on user-side SINR. We formulate a non-convex optimization problem to minimize the maximum DEP among users by jointly optimizing AP beamforming, RIS phase shifts, and blocklength allocation. A hybrid solution framework is proposed, combining deep reinforcement learning for continuous variables with a differential evolution (DE) algorithm for discrete blocklength optimization. Simulation results demonstrate the superior performance of the proposed method over alternating optimization and genetic algorithm (GA) baselines. Notably, increasing the number of AP antennas and transmission blocklength improves network availability, although gains saturate due to inter-user interference and diminishing returns. Moreover, the proposed DE-based algorithm for blocklength optimization consistently outperforms the GA method in terms of both solution quality and computational efficiency.

Reconfigurable intelligent surfaces (RIS) are emerging as a crucial technology to address the energy consumption challenges posed by the widespread deployment of access points (APs) in cell-free massive multiple-input multiple-output (CF mMIMO) systems within future sixth-generation (6G) networks. However, most existing studies on RIS-aided CF mMIMO systems assume ideal hardware and static channel conditions, which deviate from practical deployment scenarios. This work analyzes the performance of a RIS-aided CF mMIMO system by incorporating the combined effects of hardware impairments from non-ideal transceivers and channel aging caused by user mobility. We first characterize both direct and cascaded channels between APs and user equipment, modeling them using correlated Rician fading to capture realistic propagation effects. The overall channel is then estimated via the minimum mean square error method under perfect and imperfect line-of-sight phase knowledge, and we derive an analytical expression for the instantaneous spectral efficiency (SE). We also derive the closed-form expressions of the use-and-then-forget bound with the maximum-ratio transmission precoding method. Building on these insights, we establish an efficient joint optimization framework for beamforming in the AP and phase-shift adaptations in the RIS, exploring an alternating optimization method and a deep-reinforcement learning (DRL)-based algorithm. The numerical results validate our theoretical analysis, illustrating the impact of hardware impairments and channel aging on SE. Although the DRL-based method is scalable and adapts well to dynamic environments, its high computational and memory demands pose challenges for real-time deployment, highlighting a trade-off between performance and feasibility.

Movable antennas (MAs) and reconfigurable intelligent surfaces (RISs) have emerged as two promising technologies for enhancing wireless communication performance, owing to their capability to dynamically reshape and manipulate the propagation environment. Motivated by this potential, this paper investigates the joint utilization of the additional degrees of freedom introduced by MAs (through antenna repositioning) and RIS (via optimized reflection) to effectively mitigate computation distortion in over-the-air computation (AirComp) systems. Specifically, we formulate an optimization problem aimed at minimizing the mean square error (MSE) between the target function values and their estimates, through jointly optimizing the receive beamformer at the access point, RIS reflection phase shifts, and transmit coefficients as well as antenna positions of AirComp users. To address the non-convex nature of the formulated problem, we develop a computationally efficient algorithm capitalizing alternating optimization technique, the penalty-dual decomposition method, and the particle swarm optimization enhanced by a dynamic neighborhood pruning mechanism. Next, we further extend the optimization framework to a more practical case with discrete MA positions. Extensive simulation results demonstrate that the joint optimization of RIS beamforming and MA positioning substantially reduces the computation MSE, compared to the separate MA-enhanced AirComp and RIS-aided AirComp schemes. Moreover, the proposed algorithm achieves comparable performance to the penalty function-based method, while incurring significantly lower computational complexity.

Six-dimensional movable antenna (6DMA) has been identified as a new disruptive technology for future wireless systems to support a large number of users with only a few antennas. However, the intricate relationships between the signal carrier wavelength and the transceiver region size lead to inaccuracies in traditional far-field 6DMA channel model, causing discrepancies between the model predictions and the hybrid-field channel characteristics in practical 6DMA systems, where users might be in the far-field region relative to the antennas on the same 6DMA surface, while simultaneously being in the near-field region relative to different 6DMA surfaces. Moreover, due to the high-dimensional channel and the coupled position and rotation constraints, the estimation of the 6DMA channel and the joint design of the 6DMA positions and rotations and the transmit beamforming at the base station (BS) incur extremely high computational complexity. To address these issues, we propose an efficient hybrid-field generalized 6DMA channel model, which accounts for planar-wave propagation within individual 6DMA surfaces and spherical-wave propagation among different 6DMA surfaces. Furthermore, by leveraging directional sparsity, we propose a low-overhead channel estimation algorithm that efficiently constructs a complete channel map for all potential antenna position-rotation pairs while limiting the training overhead incurred by antenna movement. In addition, we propose a low-complexity design leveraging deep reinforcement learning (DRL), which facilitates the joint design of the 6DMA positions, rotations, and beamforming in a unified manner. Numerical results demonstrate the superiority of the proposed hybrid-field channel model, which achieves sum rates closely approaching that of the near-field channel model. The results also show that the proposed channel estimation algorithm can accurately recover the channel with lower computational complexity than traditional channel estimation algorithm. Moreover, the 6DMA system enhanced by the proposed DRL algorithm significantly outperforms existing flexible antenna systems, especially in the near-field region.

Cell-free (CF) massive multiple-input multiple-output (mMIMO) and reconfigurable intelligent surface (RIS) are two advanced transceiver technologies for realizing future sixth-generation (6G) networks. In this paper, we investigate the joint precoding and access point (AP) selection for an energy-efficient RIS-aided CF mMIMO system. To address the associated computational complexity and communication power consumption, we advocate for user-centric dynamic networks in which each user is served by a subset of APs rather than by all of them. Based on the user-centric network, we formulate a joint precoding and AP selection problem to maximize the energy efficiency (EE) of the considered system. To solve this complex nonconvex problem, we propose an innovative double-layer multi-agent reinforcement learning (MARL)-based scheme. Moreover, we propose an adaptive power threshold-based AP selection scheme to further enhance the EE of the considered system. To reduce the computational complexity of the RIS-aided CF mMIMO system, we introduce a fuzzy logic (FuZ) strategy into the MARL scheme to accelerate convergence. The simulation results show that the proposed FuZ-based MARL cooperative architecture effectively improves EE performance, offering a 85% enhancement over the zero-forcing (ZF) method, and achieves faster convergence speed compared with MARL. It is important to note that increasing the transmission power of the APs or the number of RIS elements can effectively enhance the spectral efficiency (SE) performance, which also leads to an increase in power consumption, resulting in a non-trivial trade-off between the quality of service and EE performance.

In this paper, we investigate a bistatic integrated sensing and communications (ISAC) system, consisting of a base station (BS) with multiple transmit antennas, a sensing receiver with multiple receive antennas, a single-antenna communication user (CU), and a point target to be sensed. Specifically, the BS transmits a superposition of Gaussian information and deterministic sensing signals to support ISAC. The BS aims to deliver information symbols to the CU, while the sensing receiver aims to estimate the target’s direction-of-arrival (DoA) with respect to the sensing receiver by processing the echo signals reflected by the target. For the sensing receiver, we assume that only the sequences of the deterministic sensing signals and the covariance matrix of the information signals are perfectly known, whereas the specific realizations of the information signals remain unavailable. Under this setup, we first derive the corresponding Cram´er-Rao bounds (CRBs) for DoA estimation and propose practical estimators to accurately estimate the target’s DoA. Subsequently, we formulate the transmit beamforming design as an optimization problem aiming to minimize the CRB, subject to a minimum signal-to-interference-plus-noise ratio (SINR) requirement at the CU and a maximum transmit power constraint at the BS. When the BS employs only Gaussian information signals, the resulting beamforming optimization problem is convex, enabling the derivation of an optimal solution. In contrast, when both Gaussian information and deterministic sensing signals are transmitted, the resulting problem is non-convex and a locally optimal solution is acquired by exploiting successive convex approximation (SCA). Finally, numerical results demonstrate that the utilization of additional deterministic sensing signals is critical for sensing performance enhancement, while solely employing Gaussian information signals leads to a notable performance degradation for target sensing. It is unveiled that the proposed transmit beamforming design achieves a superior ISAC performance boundary compared with various benchmark schemes.

In this paper, we investigate a wireless powered communication network (WPCN) in which a multi-antenna hybrid access point (HAP) communicates with multiple Internet-of-Things (IoT) devices, assisted by an active simultaneously transmitting and reflecting reconfigurable intelligent surface (aSTAR-RIS). In the energy transfer (ET) phase, the IoT devices harvest energy from the HAP with a nonlinear energy harvesting (EH) model, and subsequently transmit information signals to the HAP during the information transmission (IT) phase. To explore its full potential, the aSTAR-RIS employs energy splitting (ES), mode switching (MS), and time switching (TS) protocols. A sum rate maximization problem is formulated for each protocol, which jointly optimize the beamforming at the HAP, allocation of time slots and transmitting power for the IoT devices, and the adaptation of the aSTAR-RIS coefficients. To address the optimization problem with multiple coupled variables and complex non-convex constraints, we firstly decompose it into several subproblems. Specifically, to optimize the coefficients of the aSTAR-RIS in the IT phase, we develop a fractional programming-based successive convex approximation algorithm to handle the fractional objective function and the minimum rate constraints. Moreover, to obtain the coefficients of the aSTAR-RIS during the ET phase, we design a penalty-based SCA algorithm to address the binary constraints in the MS protocol and the rank-one constraints. Numerical results demonstrate that 1) employing the aSTAR-RIS in WPCNs can realize the extraordinary sum rate gain in comparison with the benchmarks of the active RIS and the passive STAR-RIS; 2) among the three operation protocols, the ES demonstrates the best performance, with the MS following closely behind, while the TS is the least effective; 3) as the minimum required data rate for each IoT device decreases, the performance gap among the three protocols becomes narrower.

This paper investigates the information-theoretic energy efficiency of intelligent reflecting surface (IRS)-aided wireless communication systems, taking into account the statistical quality-of-service (QoS) constraints on delay violation probabilities. Specifically, effective capacity is adopted to capture the maximum constant arrival rate that can be supported by a time-varying service process while fulfilling these statistical QoS requirements. We derive the minimum bit energy required for the IRS-aided wireless communication system under QoS constraints and analyze the spectral efficiency and energy efficiency tradeoff at low but nonzero signal-to-noise ratio (SNR) levels by also characterizing the wideband slope values. Our analysis demonstrates that the energy efficiency for the considered system under statistical QoS constraints can approach that for a system without QoS limitations in the low-SNR regime. Additionally, deploying a sufficiently large number of practical IRS reflecting elements can substantially reduce energy consumption required to achieve desired spectral efficiency performance in the low-power regime, even with limited bit-resolution phase shifters. Besides, we reveal that compared with the results applied to the low-power regime, higher effective capacity performance can be achieved in scenarios with sparse multipath fading while achieving the same minimum bit energy in the wideband regime.

Movable antenna (MA) technology has recently attracted significant research attention as a promising solution for enhancing wireless network performance. However, conventional MAs can only effectively serve users in close proximity, resulting in restricted coverage. To overcome this limitation, in this paper, we explore a beyond-diagonal reconfigurable intelligent surface (BD-RIS)-aided MA system. First, we propose a penalty-based block coordinate descent optimization algorithm tailored to the new constraints imposed by BD-RIS-aided MA systems. Specifically, our method decouples the inherently non-convex and coupled antenna distance constraints by introducing auxiliary optimization variables. Subsequently, the resulting problem is efficiently addressed via alternating optimization, with closed-form updates for the auxiliary variables. Furthermore, recognizing the challenges posed by large-scale BD-RIS deployments, which have the potential for serving a substantial number of users, traditional centralized optimization frameworks encounter considerable difficulties, including high computational complexity, excessive communication overheads, as well as limited scalability with increasing system size. To address these limitations, we propose an efficient asynchronous alternating direction method of multipliers (AS-ADMM) scheme aimed at maximizing the sum rate. Our numerical results demonstrate that the BD-RIS-aided MA system achieves superior performance compared to both conventional fixed position antenna and BD-RIS-aided systems. Furthermore, the proposed AS-ADMM framework can achieve a trade-off between performance and computational overhead, highlighting its potential for practical implementation in large-scale wireless communication networks.

This paper investigates the downlink energy efficiency (EE) optimization for cell-free massive multiple input multiple output (CF-mMIMO) systems subject to ultra-reliable and low-latency communication (URLLC) requirements. To achieve superior performance, we jointly consider the impacts of power allocation, access point (AP)-user association, and AP sleep modes under the finite blocklength (FBL) regime, leading to a challenging mixed-integer (MI) non-convex optimization problem. Utilizing a sequential convex approximation (SCA) framework, we first propose the SCA-Relaxation algorithm to convert the original problem into a series of second-order cone programming (SOCP) sub-problems, which can be efficiently addressed via modern convex programming solvers. Moreover, for further reducing computational complexity, we approximate the original problem as a continuous-variable optimization and tackle it via a combination of the Dinkelbach transformation, penalty functions, as well as an accelerated proximal gradient method with adaptive momentum, resulting in the proposed low complexity EE maximization (LCEE-max) algorithm. Besides, the related convergence and complexity analysis of these two algorithms are also presented in detail. Simulation results demonstrate that compared to the state-of-the-art baseline algorithm, the proposed two algorithms achieve the EE improvements of approximately 40% and 30%, respectively, along with a substantial reduction in complexity, thereby enabling efficient and fast resource allocation in CF-mMIMO-enabled URLLC scenarios.

We propose an innovative physical layer authentication method, leveraging deep learning to robustly safeguard millimeter wave communications against pilot contamination and clone attacks. Unlike traditional upper-layer authentication mechanisms, our method capitalizes on the spatial-temporal characteristics of millimeter wave channels to extract unique fingerprints, thus establishing a lightweight channel-based authentication technique. Existing methods largely overlook pilot contamination attacks, which may severely degrade the performance of physical layer authentication. Furthermore, traditional threshold-based methods struggle to differentiate between multiple nodes, while supervised learning-based methods are practically constrained due to the unavailability of attackers’ instantaneous channel state information. Moreover, traditional real-valued deep neural networks are inefficient in utilizing the phase information of complex-valued channels, rendering them inadequate for designing practical physical layer authentication schemes. To address these challenges, we propose an autoencoder, empowered by an alternating direction method of multipliers, which can detect and mitigate pilot contamination attacks by exploiting the inherent sparsity of channels. Subsequently, we design a weighted loss function to optimize the proposed classifiable autoencoder to strike an effective balance between detecting clone attacks and authenticating multiple nodes. Finally, to further enhance feature extraction from complex-valued channels, we customize a complex-valued classifiable autoencoder incorporating an innovative complex-valued long short-term memory module. Our simulation results unveil that the proposed method significantly outperforms existing approaches in maintaining high authentication accuracy even under pilot contamination, achieving a desirable trade-off between false alarm and detection rates. Additionally, our proposed complex-valued neural networks further enhance the accuracy of clone attack detection and multiple legitimate nodes authentication.

Deep learning (DL) has become pivotal in advancing integrated sensing and communication (ISAC) systems. However, conventional DL models often require frequent updating or retraining to adapt to dynamic ISAC environments. To address these limitations, this work creatively proposes a large language model (LLM)-based ISAC system, called LLM-ISAC, to enable concurrent sensing-communication processing in a unified framework, with enhanced generalization and environmental robustness. To realize LLM-ISAC, we design a novel signal encoder to transform ISAC signals into LLM-compatible representations through a delay-Doppler-spatial transformer, enabling discriminative cross-domain signal feature extraction for downstream tasks. Moreover, we develop an innovative ISAC-specific context prompt to construct structured machine-readable prompts, dynamically guiding the LLM’s reasoning without retraining and ensuring robust generalization to unseen scenarios. To the best of the authors’ knowledge, this is the first work leveraging the property of LLM in ISAC systems. Extensive simulations demonstrate that LLMI-SAC achieves significant superiority in sensing accuracy, communication reliability, and environmental robustness, compared to state-of-the-art DL-based ISAC methods.

The enhancement of spectrum efficiency and the realization of secure spectrum utilization are critically dependent on spectrum cognition. However, existing spectrum cognition methods often exhibit limited generalization and suboptimal accuracy when deployed across diverse spectrum environments and tasks. To overcome these challenges, we propose a spectrum foundation model, termed SpectrumFM, which provides a new paradigm for spectrum cognition. An innovative spectrum encoder that exploits the convolutional neural networks and the multi-head self attention mechanisms is proposed to effectively capture both fine-grained local signal structures and high-level global dependencies in the spectrum data. To enhance its adaptability, two novel self-supervised learning tasks, namely masked reconstruction and next-slot signal prediction, are developed for pre-training SpectrumFM, enabling the model to learn rich and transferable representations. Furthermore, low-rank adaptation (LoRA) parameter-efficient fine-tuning is exploited to enable SpectrumFM to seamlessly adapt to various downstream spectrum cognition tasks, including spectrum sensing (SS), anomaly detection (AD), and wireless technology classification (WTC). Extensive experiments demonstrate the superiority of SpectrumFM over state-of-the-art methods. Specifically, it improves detection probability in the SS task by 30% at -4 dB signal-to-noise ratio (SNR), boosts the area under the curve (AUC) in the AD task by over 10%, and enhances WTC accuracy by 9.6%.1

In this paper, we propose a novel cross-domain channel estimation (CDCE) algorithm for orthogonal frequency division multiplexing (OFDM) systems, leveraging the unique characteristics of the delay-Doppler (DD) domain channel. Specifically, the proposed algorithm transforms the time-frequency (TF) domain pilot sequence of OFDM into the DD domain and applies a two-dimensional (2D) twisted-convolution for acquiring a coarse estimation of the underlying channel delay and Doppler. Then, the OFDM channel estimation is formulated as a sparse signal recovery problem in the TF domain according to the dictionary derived based on the obtained delay and Doppler estimates. Furthermore, a low-complexity ℓ1-regularized least-square estimator is proposed to effectively solve this problem. Moreover, we further develop a performance analysis framework of the proposed scheme based on the ambiguity function (AF) of the adopted pilot sequence. Our numerical results demonstrate noticeable estimation performance improvement compared to conventional OFDM channel estimation methods, particularly in the presence of high channel mobility.

In this paper, we propose a novel polarized six-dimensional movable antenna (P-6DMA) to enhance the performance of wireless communication cost-effectively. Specifically, the P-6DMA enables polarforming by adaptively tuning the antenna’s polarization electrically as well as controls the antenna’s rotation mechanically, thereby exploiting both polarization and spatial diversity to reconfigure wireless channels for improving communication performance. First, we model the P-6DMA channel in terms of transceiver antenna polarforming vectors and antenna rotations. We then propose a new two-timescale transmission protocol to maximize the weighted sumrate for a P-6DMA-enhanced multiuser system. Specifically, antenna rotations at the base station (BS) are first optimized based on the statistical channel state information (CSI) of all users, which varies at a much slower rate compared to their instantaneous CSI. Then, transceiver polarforming vectors are designed to cater to the instantaneous CSI under the optimized BS antennas’ rotations. Under the polarforming phase shift and amplitude constraints, a new polarforming and rotation joint design problem is efficiently addressed by a low-complexity algorithm based on penalty dual decomposition, where the polarforming coefficients are updated in parallel to reduce computational time. Simulation results demonstrate the significant performance advantages of polarforming, antenna rotation, and their joint design in comparison with various benchmarks without polarforming or antenna rotation adaptation.

The reconfigurability of fluid antenna systems (FASs) and reconfigurable intelligent surfaces (RISs) can be jointly utilized to achieve unprecedented degrees of freedom for wireless communication systems. However, adjusting fluid antennas and RISs based on instantaneous channel state information (CSI) is highly challenging. To tackle this challenge, we propose a two-timescale approach for FAS-RIS-aided multi-user systems with regularized zero-forcing (RZF)/zero-forcing (ZF) precoding, where only statistical CSI is required for FAS and RIS optimization. To achieve this goal, we first obtain the closed-form evaluation for the ergodic sum rate (ESR) of FAS-RIS aided multi-user systems with RZF/ZF precoding by exploiting random matrix theory (RMT). Then, we propose an ESR maximization algorithm by jointly optimizing the port selection for FASs, phase shifts at the RIS, and regularization factor of RZF. Numerical results validate the approximation accuracy of the derived ESR evaluation and demonstrate that the performance enhancement benefiting from the joint design of FASs and RISs becomes more prominent when the number of users becomes larger.

Leveraging its exceptionally wide bandwidth, Terahertz (THz) communication offers ultra-high-speed data transmission and remarkably precise sensing, establishing itself as a cornerstone technology for integrated sensing and communication (ISAC) systems. This paper investigates a multi-base-station (multi-BS) cooperative extremely-large-scale multiple-input multiple-output (XL-MIMO) orthogonal frequency division multiplexing (OFDM) near-field THz-ISAC system designed to guarantee secure downlink communication for multiple users, while simultaneously enhancing multi-target localization accuracy. The core challenge lies in optimizing the secrecy rate subject to the Cramer-Rao Bound (CRB) constraint to strike an´ effective balance between communication security and sensing precision. To this end, we propose an unsupervised learning-based complex-valued deep neural network (CVNN) that jointly optimizes hybrid beamforming and radar sensing signals in a data-driven manner. Simulation results unveil that the proposed approach outperforms the conventional alternating optimization-based hybrid beamforming benchmark in terms of secrecy rate and computation time. These results validate the practicality of data-driven joint optimization in THz-ISAC systems by demonstrating its effectiveness in achieving an efficient tradeoff between communication security and sensing accuracy, while also providing actionable design guidelines for scalable, low-latency system in extremely-large-scale deployment.

This work proposes a framework for safeguarding probabilistic communications from a transmitter Alice to a receiver Bob in the presence of a power-constrained eavesdropper Eve, where Eve awakens with a prior probability$\lambda$and employs a detection-and-then-decoding strategy to eavesdrop on Alice's transmissions. We first optimally design Eve's wake-up probability$\lambda$and her detection threshold to achieve the maximum overall secrecy outage probability$p_{\text{os }}^{*}$subject to her average power consumption budget. Our analysis proves that the proposed detection-and-then-decoding strategy requires less power to keep Eve consistently awake for eavesdropping compared to a conventional direct-decoding strategy. Subsequently, from the perspective of Alice, the optimal transmit power and redundancy rate are determined to maximize the effective transmission rate subject to the maximum tolerable secrecy outage probability and Alice's maximum transmit power. We explicitly show that the achievable confidentiality$1-p_{\text{os }}^{*}$is a combination of communication covertness, measured by the probability that Eve fails to detect Alice's transmission, and communication secrecy, measured by the probability that Eve fails to decode Alice's communication. Our results unveil the non-trivial tradeoff between the achieved covertness and secrecy with respect to the power-constrained Eve.

Deep learning (DL) has emerged as a key enabler for unlocking the potential of integrated sensing and communication (ISAC). Despite recent progress, current DL methods primarily handle sensing and communication as independent tasks, overlooking potential performance enhancement through a joint approach. Moreover, existing methods rely on fully annotated data for training, which is often challenging to obtain, especially in multi-task scenarios where labeled data may be scarce or only exist for a subset of tasks. Motivated by these shortcomings, this paper proposes a novel scheme, MTL-DFM, to enable simultaneous sensing and communication with partially labeled training data, which leverages multi-task learning (MTL) and a diffusion model (DFM). In particular, we introduce an initial feature extraction module (IFEM) to jointly capture shared information across tasks and explore inherent cross-task connections for enhanced feature extraction. Next, we design a signal denoising with incomplete labeling (SDIL) module to effectively remove noise from extracted information and construct comprehensive feature representations for all tasks with partially labeled datasets, which is difficult for conventional DL methods. Simulation results verify the superior performance offered by MTL-DFM over prior state-of-the-art methods.

This paper investigates integrated localization and communication in a multi-cell system, and proposes a coordinated beamforming algorithm to enhance target localization accuracy while preserving communication performance. Within this integrated sensing and communication (ISAC) system, the CramérRao lower bound (CRLB) is adopted to quantify the accuracy of target localization, with its closed-form expression derived for the first time. It is shown that the nuisance parameters can be disregarded without impacting the CRLB of time of arrival (TOA)based target localization. Capitalizing on the derived CRLB, we formulate a nonconvex coordinated beamforming problem to minimize the CRLB while satisfying signal-to-interference-plusnoise ratio (SINR) constraints in communication. To facilitate the development of solution, we reformulate the original problem into a more tractable form and solve it through semi-definite programming (SDP). Notably, we show that the proposed algorithm can always obtain rank-one global optimal solutions under mild conditions. Finally, numerical results demonstrate the superiority of the proposed algorithm over benchmark algorithms and reveal the performance trade-off between localization accuracy and communication SINR.

Integrated sensing and communication (ISAC) can boost the spectrum efficiency and facilitate the diverse emerging applications via sharing the same spectrum and hardware between communication and sensing. However, it may suffer from high complexity. In this paper, we develop a low-complexity deep unfolding learning aided transceiver design for ISAC. Particularly, the weighted sum of multi-user interference power and the reciprocal of sensing signal-to-interference-plus-noise ratio is minimized subject to the constraints of constant modulus signal and waveform similarity by transceiver design. An alternating direction method of multipliers (ADMM)-based iterative algorithm is first developed to solve this non-convex optimization problem. To reduce the complexity, we propose a deep unfolding neural network (NN), which can unfold the underlying ADMMbased iterative algorithm to a lightweight NN with some learnable parameters and circumvent the bisection method using the projected gradient descent. Simulation results demonstrate the effectiveness of our proposed deep unfolding NN.

Navigating autonomous vehicles in open scenarios is a challenge due to the difficulties in handling unseen objects. Existing solutions either rely on small models that struggle with generalization or large models that are resource-intensive. While collaboration between the two offers a promising solution, the key challenge is deciding when and how to engage the large model. To address this issue, this paper proposes opportunistic collaborative planning (OCP), which seamlessly integrates efficient local models with powerful cloud models through two key innovations. First, we propose large vision model guided model predictive control (LVM-MPC), which leverages the cloud for LVM perception and decision making. The cloud output serves as a global guidance for a local MPC, thereby forming a closed-loop perception-to-control system. Second, to determine the best timing for large model query and service, we propose collaboration timing optimization (CTO), including object detection confidence thresholding (ODCT) and cloud forward simulation (CFS), to decide when to seek cloud assistance and when to offer cloud service. Extensive experiments show that the proposed OCP outperforms existing methods in terms of both navigation time and success rate.

Dense wireless local area networks (WLANs) have been developed to enable high-capacity indoor wireless communications. Nevertheless, due to the inherent interference among densely deployed access points (APs) and the competitive channel access scheme, the balance of network load among APs may degrade significantly. This challenge motivates the development of a novel association policy exploiting the integrated sensing and communication (ISAC) technique to enhance network fairness. In this paper, we propose a novel optimization framework for ISAC resource allocation in the downlink of dense WLANs to maximize the total network fairness utility function while guaranteeing users' quality-of-service (QoS) requirements. Unlike conventional association policies, our proposed approach effectively determines the associated APs based on signal-to-noise ratio (SNR) and measured angle of sensing signals to ensure network fairness. By leveraging coalition game and binary relaxation techniques, we further transform the non-convex resource allocation design problem and address them via the alternating optimization (AO) technique. Simulation results demonstrate that the proposed ISAC-based resource allocation framework can effectively improve the data rate over the network and, simultaneously ensure fairness among stations.

This paper studies resource allocation for a reconfigurable intelligent surface (RIS)-assisted mixed near- and far-field communication utilizing a directional antenna. The resource allocation algorithm is designed as a non-convex optimization problem to maximize the data rate in the communication system. To circumvent the problem intractability, the non-convex problem is transformed into a standard semidefinite relaxation (SDR) programming problem by exploiting radiation field electromagnetic theory. This allows us to characterize the solution structure of the joint height and axis of the directional antenna and the RIS phase shift matrix that facilitates the design of an efficient iterative algorithm for obtaining the solution. We reveal that the maximal data rate of RIS-assisted mixed near- and far-field communication is achieved if the axis of the directional antenna aligns the plane that includes both the center of the RIS and the UE's location. Simulation results demonstrate a significant data rate improvement with the proposed resource allocation compared to the two baseline schemes.

This paper investigates resource allocation design for wideband integrated sensing and communication (ISAC) systems. We aim to minimize the Cramér-Rao Bound (CRB) for target estimation by jointly optimizing subcarrier allocation, digital beamforming matrices, and frequency-independent and frequency-dependent analog beamforming matrices at the base station (BS) adopting a hybrid beamforming structure. We formulate the optimization design as a non-convex mixed-integer nonlinear programming (MINLP) problem, subject to the transmit power budget constraint of the BS, the rate quality-of-service (QoS) constraints for users, and the discrete nature of the analog beamformer. To achieve a globally optimal solution for the complicated design problem, an iterative resource allocation algorithm is proposed by exploiting the generalized Bender's decomposition (GBD) method. Our simulation results demonstrate the crucial importance of simultaneously optimizing all available degrees-of-freedom (DoFs) in wideband ISAC systems jointly and optimally. Besides, our results unveil that deploying true-time-delayer (TTD) units with limited bit-resolution time delays can achieve substantial gains in both communication and sensing performances.

Hybrid digital-analog precoding is a pivotal transmission technique to balance communication performance and hardware costs associated with radio frequency (RF) chains in millimeter wave (mmWave) massive multiple-input multipleoutput (MIMO). However, most existing designs utilize Shannon rate and assume an infinite blocklength, which is impractical for emerging finite blocklength (FBL) applications, such as massive machine-type communications. To fill in this gap, this paper investigates hybrid precoding optimization in the FBL regime. The aim is to maximize the weighted sumrate (WSR), while fulfilling the transmit power budget at the base station (BS) and users' minimum rate requirements. The formulated optimization problem is highly challenging to solve, particularly due to the complex and nonconcave FBL rate function and the intricate coupling between analog and digital precoders. To tackle these issues, we propose a computationally efficient solution based on the penalty dual decomposition (PDD) method, which is guaranteed to converge to the Karush-KuhnTucker (KKT) solutions under mild conditions. Simulation results demonstrate that our proposed hybrid precoding design significantly outperforms several baseline schemes, especially those ignoring the impact of blocklength and adopting Shannon rate as the performance metric.

The concept of extremely large-scale reconfigurable intelligent surfaces (XL-RIS) holds great promise for enabling sixth-generation (6G) communications. However, the vast number of passive reflection coefficients and the transition from far-field to near-field electromagnetic radiation pose significant challenges for channel estimation, especially under tight pilot overhead constraints. To address these challenges, we propose a novel hybrid integrated sensing and communication architecture and a three-stage channel estimation scheme for XL-RIS-assisted millimeter wave communication systems. The proposed scheme leverages user position data, obtained through a sensing module, to accurately estimate near-field cascaded channels. First, we design an integrated base station architecture that combines a fully-digital sensing module with a hybrid communication module to achieve high-resolution distance and angle estimations using linear frequency modulation signals. Next, we introduce a distance-error-minimization based localization algorithm to effectively estimate user coordinates. To balance channel estimation performance and pilot overhead, we carefully select the appropriate number of position update iterations. Using these estimated coordinates, we calculate the channel fading coefficients for the near-field cascaded channels, facilitating accurate channel estimation. Simulation results validate the effectiveness of our proposed scheme, demonstrating reduced overhead while maintaining superior channel estimation performance.

Immersive communication, including emerging augmented reality, virtual reality, and holographic telepresence, has been identified as a key service for enabling next-generation wireless applications. To align with legacy wireless applications, such as enhanced mobile broadband or ultra-reliable low-latency communication, network slicing has been widely adopted. However, attempting to statistically isolate the above types of wireless applications through different network slices may lead to throughput degradation and increased queue backlog. To address these challenges, we establish a unified QoS-aware framework that supports immersive communication and legacy wireless applications simultaneously. Based on the Lyapunov drift theorem, we transform the original long-term throughput maximization problem into an equivalent short-term throughput maximization weighted by virtual queue length. Moreover, to cope with the challenges introduced by the interaction between large-timescale network slicing and short-timescale resource allocation, we propose an adaptive adversarial slicing (Ad2S) scheme for networks with invarying channel statistics. To track the network channel variations, we also propose a measurement extrapolation-Kalman filter (ME-KF)-based method and refine our scheme into Ad2S-non-stationary refinement (Ad2S-NR). Through extended numerical examples, we demonstrate that our proposed schemes achieve 3.86 Mbps throughput improvement and 63.96% latency reduction with 24.36% convergence time reduction. Within our framework, the trade-off between total throughput and user service experience can be achieved by tuning systematic parameters.

The increase in antenna apertures and transmission frequencies in next-generation wireless networks is catalyzing advancements in near-field communications (NFC). In this paper, we investigate secure transmission in near-field multi-user multiple-input single-output (MU-MISO) scenarios. Specifically, with the advent of extremely large-scale antenna arrays (ELAA) applied in the NFC regime, the spatial degrees of freedom in the channel matrix are significantly enhanced. This creates an expanded null space that can be exploited for designing secure communication schemes. Motivated by this observation, we propose a near-field dynamic hybrid beamforming architecture incorporating artificial noise, which effectively disrupts eavesdroppers at any undesired positions, even in the absence of their channel state information (CSI). Furthermore, we comprehensively analyze the dynamic precoder’s performance in terms of the average signal-to-interference-plus-noise ratio, achievable rate, secrecy capacity, secrecy outage probability, and the size of the secrecy zone. In contrast to far-field secure transmission techniques that only enhance security in the angular dimension, the proposed algorithm exploits the unique properties of spherical wave characteristics in NFC to achieve secure transmission in both the angular and distance dimensions. Remarkably, the proposed algorithm is applicable to arbitrary modulation types and array configurations. Numerical results demonstrate that the proposed method achieves approximately 20% higher rate capacity compared to zero-forcing and the weighted minimum mean squared error precoders.

Future sixth-generation (6G) systems are expected to leverage extremely large-scale multiple-input multiple-output (XL-MIMO) technology, which significantly expands the range of the near-field region. The spherical wavefront characteristics in the near field introduce additional degrees of freedom (DoFs), namely distance and angle, into the channel model, which leads to unique challenges in channel estimation (CE). In this paper, we propose a new sensing-enhanced uplink CE scheme for near-field XL-MIMO, which notably reduces the required quantity of baseband samples and the dictionary size. In particular, we first propose a sensing method that can be accomplished in a single time slot. It employs power sensors embedded within the antenna elements to measure the received power pattern rather than baseband samples. A time inversion algorithm is then proposed to precisely estimate the locations of users and scatterers, which offers a substantially lower computational complexity. Based on the estimated locations from sensing, a novel dictionary is then proposed by considering the eigen-problem based on the near-field transmission model, which facilitates efficient near-field CE with less baseband sampling and a more lightweight dictionary. Moreover, we derive the general form of the eigenvectors associated with the near-field channel matrix, revealing their noteworthy connection to the discrete prolate spheroidal sequence (DPSS). Simulation results unveil that the proposed time inversion algorithm achieves accurate localization with power measurements only, and remarkably outperforms various widely-adopted algorithms in terms of computational complexity. Furthermore, the proposed eigen-dictionary considerably improves the accuracy in CE with a compact dictionary size and a drastic reduction in baseband samples by up to 66%.

In this paper, we consider an unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) network, where multiple UAVs with caching and computation functionalities are deployed to satisfy the heterogeneous content and service requests from the user equipments (UEs). In order to comprehensively characterize the capability of our considered network in satisfying the UEs’ requests, we define the weighted sum of the content cache hit ratio and the service delay shrinkage ratio as the average quality-of-experience (QoE) of our network and adopt it as the performance metric. Through analysis, we show how the average QoE of our network is dependent on the content cache and service placement decisions at the UAVs, as well as the computation task offloading decisions at the UEs, thus enabling us to formulate an average QoE maximization problem, subject to practical constraints on the UAVs’ caching and computation capabilities. To solve this NP-hard problem, we decompose it into two sub-problems, namely, the content cache and service placement optimization sub-problem and the task offloading optimization sub-problem. Gibbs sampling-based and matching game-based algorithms are proposed to efficiently solve these sub-problems iteratively. Via numerical results, we validate the effectiveness of our proposed algorithms. Compared to various benchmarks, we demonstrate that our proposed algorithms can significantly improve the average QoE of our considered network, especially when the caching and computation resources of the UAVs are limited.

As a large variety of applications emerge in vehicle-to-infrastructure (V2I) networks, the explosion of data places greater demands on communications and computing. Sensing technology offers accurate data by collecting real-time environmental information. Meanwhile, mobile edge computing (MEC) can significantly reduce communication latency and enhance computational efficiency. Therefore, integrating the two novel technologies into V2I applications to improve overall performance has become a research hotspot. In this paper, we focus on the optimization problem for determining caching, offloading, and matching strategies to minimize system cost under the joint communications, sensing, and MEC framework of V2I networks. First, we analyze the positive impact of sensing on signaling overhead and age of information (AoI), and derive a linear relationship between delay and AoI. Next, we formulate the optimization function of system cost, which is defined as the weighted sum of AoI and energy consumption and is proved to be NP-hard. To address this problem, we leverage an improved quantum particle swarm optimization (QPSO) algorithm to acquire a suboptimal solution of caching and offloading strategies. This significantly reduces computational complexity compared to the optimal solution obtained via the branch-and-bound (B&B) method. According to the vehicles’ AoI, we design a matching algorithm for the roadside unit (RSU) with vehicles. Building on these, we propose a QPSO-based algorithm under the joint communications, sensing, and MEC framework (QJCSM). Simulation results demonstrate that the QJCSM algorithm outperforms other baseline algorithms in terms of AoI and energy consumption and achieves near-optimal performance with low complexity.

This paper proposes a joint active and passive beamforming design for reconfigurable intelligent surface (RIS)-aided wireless communication systems, adopting a piecewise near-field channel model. While a traditional near-field channel model, applied without any approximations, offers higher modeling accuracy than a far-field model, it renders the system design more sensitive to channel estimation errors (CEEs). As a remedy, we propose to adopt a piecewise near-field channel model that leverages the advantages of the near-field approach while enhancing its robustness against CEEs. Our study analyzes the impact of different channel models, including the traditional near-field, the proposed piecewise near-field and far-field channel models, on the interference distribution caused by CEEs and model mismatches. Subsequently, by treating the interference as noise, we formulate a joint active and passive beamforming design problem to maximize the spectral efficiency (SE). The formulated problem is then recast as a mean squared error (MSE) minimization problem and a suboptimal algorithm is developed to iteratively update the active and passive beamforming strategies. Simulation results demonstrate that adopting the piecewise near-field channel model leads to an improved SE compared to both the near-field and far-field models in the presence of CEEs. Furthermore, the proposed piecewise near-field model achieves a good trade-off between modeling accuracy and system’s degrees of freedom (DoF).

Orthogonal time frequency space (OTFS) modulation has received significant attention recently due to its superior performance compared to conventional multicarrier waveforms. However, symbol detection with OTFS is significantly more involved and typically operates on large signal blocks with intersymbol interference (ISI) in the delay-Doppler (DD) domain. In this paper, we investigate the performance of OTFS within the cross-domain iterative detection (CDID) framework. Specifically, three distinct CDID algorithms are presented and investigated, which estimate/detect the information symbols iteratively across the frequency and DD domains via passing either thea posteriorior extrinsic information using a full-sized or single-tap linear minimum mean square error (LMMSE) estimator. Building upon this framework, we study the average mean square error (MSE) for the considered CDID algorithms, where both the bias evolution and the state (variance) evolution are investigated. Particularly, we show that the proposed CDIDs can provide unbiased estimation under certain channel conditions. Furthermore, a fixed point exists in the state evolution when the estimation is unbiased, indicating that the algorithm’s convergence is guaranteed. More importantly, we reveal that passing thea posterioriinformation is more beneficial when the underlying channel has negligible Doppler spread while passing the extrinsic information is more suitable for non-negligible Doppler spread cases, where the frequency domain channel matrix lacks diagonal dominance. Our numerical results confirm our analytical findings and unveil the near-optimal error performance achieved by the proposed design.

Spectrum sharing unmanned aerial vehicle (UAV) network is a promising technology for future communication systems to mitigate the spectrum scarcity problem. However, the future sixth-generation large-scale wireless communication networks are expected not only to provide a high data rate for massive numbers of users but also to meet their stringent service requirements. Particularly in dynamic spectrum sharing UAV networks, the coupling of multi-dimensional resources and diverse user demands make the efficient and real-time resource allocation exceptionally challenging. A partially observable deep multi-agent active inference (PODMAI) framework is proposed to tackle these issues. The variational free energy is minimized to update the policy exploiting the belief based learning method. A decentralized training and execution multi-agent strategy is designed to navigate the challenges posed by partially observable information. To further satisfy the dynamic user demand and supplement partial observations, a joint spatial-temporal-attention prediction network is designed to construct the demand prediction enhanced PODMAI framework for resource allocation. Exploiting the established framework, an intelligent spectrum allocation and trajectory optimization scheme is elaborated for a spectrum sharing UAV network with multi-modal dynamic transmission rate demands. Simulation results demonstrate that our proposed scheme outperforms benchmark schemes in terms of the network sum transmission rate. Additionally, our proposed scheme exhibits faster convergence compared to the conventional reinforcement learning. Overall, our proposed framework can enrich intelligent resource allocation frameworks and pave the way for realizing real-time resource allocation.

Mobile edge computing (MEC) has emerged as a disruptive paradigm that facilitates effective offloading from clouds and enables processing tasks near users. With the surge of mobile data traffic and escalating demands for responsive wireless services, it becomes imperative to enhance trust, security, and efficiency within MEC environments. Blockchain technology, renowned for its immutability, transparency, and security, has proven to be a compelling solution for securing data, enhancing supervision, and fostering trusted collaborations among heterogeneous MEC stakeholders. Despite these advantages, integrating blockchain with MEC also introduces a substantial efficiency bottleneck. Specifically, the blockchain consensus process can result in the aging of critical MEC system information, causing users to perform suboptimal service decisions, thus risking a degradation in service performance. To analyze this bottleneck, we first explore a blockchain-based MEC model that ensures secure task processing across diverse stakeholders. We employ the practical Byzantine fault tolerance (PBFT) consensus to validate and share service statuses, providing critical on-chain references for users to select their preferred target MEC servers. We then introduce the age-of-information (AoI) as a metric of freshness to characterize the aging of status reports, identifying variable consensus delay as a key factor affecting AoI. We reveal the critical impact of AoI on MEC service performance through analysis, challenging the notion that a lower AoI always leads to better performance. Finally, we validate our analysis and findings through comprehensive simulations.

In this paper, a point-to-point (P2P) orthogonal time frequency space (OTFS)-based multiple-input multiple-output (MIMO-OTFS) transmission scheme is devised for millimeter wave (mmWave) channels. The proposed transmission scheme relies on a low-complexity hybrid digital-analog beamforming (HBF) scheme that exploits the delay-Doppler (DD) domain channel properties, where detailed design criteria for different channel conditions are presented, including the case where paths are indistinguishable by angles. Thanks to the proposed HBF scheme, approximate path-wise interference-free transmission of multiple data streams is achieved, and consequently, only little pre-equalization is required for combating the residual channel impairments. The achievable rate of the proposed scheme is studied and compared with the orthogonal frequency-division multiplexing (OFDM) counterpart. In particular, we unveil that the condition number of the effective angular domain matrix for OTFS is smaller than that for OFDM, due to the enhanced path separability in the DD domain. As a result, the proposed MIMO-OTFS transmission scheme demonstrates superior performance over the MIMO-OFDM transmission scheme. Our numerical results corroborate our theoretical analysis and show a near-optimal rate performance with significantly reduced complexity compared to the optimal singular value decomposition (SVD) precoding method.

In this paper, we investigate the downlink distributed transmit beamforming problem in partially connected cell-free massive multiple-input multiple-output (CF mMIMO) systems, specifically designed to satisfy the stringent requirements of ultra-reliable and low-latency communication (URLLC) services. First, we propose a scalable framework that incorporates partial access points (APs) to serve active user equipment (UE), with a reduced energy consumption and computational complexity. To this end, a min-max optimization problem is formulated for minimizing the decoding error probability (DEP) among URLLC services. Then, a graph neural network (GNN)-based strategy called G4PCF is proposed for partially connected CF mMIMO, which takes into account the underlying characteristics of the problem. Furthermore, by leveraging the temporal correlation in channel state information acquired from the previous frame, we develop a parallel G4PCF (P-G4PCF) scheme that significantly reduces both the signaling overhead and computation delay for minimizing DEP of the worst UE. Simulation results demonstrate that the proposed G4PCF and P-G4PCF architectures exhibit excellent scalability for CF mMIMO networks, offering superior performance over existing methods in terms of quality of service outage probability. Notably, P-G4PCF excels in supporting URLLC services with short frame durations and highly correlated channels, while G4PCF performs better under lower channel correlation. Moreover, the proposed algorithms can significantly enhance the application of GNNs into CF mMIMO systems with a reduced complexity compared with the classical weighted minimum mean-squared error algorithm, especially with delay sensitive services.

Orthogonal time frequency space (OTFS) has been widely acknowledged as a promising wireless technology for challenging transmission scenarios, including high-mobility channels. In this paper, we investigate the uplink multi-user OTFS transmission designs based on statistical channel information. Specifically, we investigate the pilot power allocation based on the a priori statistical channel state information (CSI) only, where performance on channel estimation is considered. We first derive the a posteriori Cram$\acute {\text {e}}$r-Rao bound (PCRB) based on the a priori channel information of each user. We unveil that the PCRB only relates to the user’s pilot signal-to-noise ratio (SNR) and the maximum of delay and Doppler shifts under the practical power-delay and power-Doppler profiles. Furthermore, a pilot power allocation scheme is proposed to minimize the average PCRB of different users, whose closed-form optimal allocation solution is derived. Moreover, we study the impact of statistical CSI on transmission rates, where a tight approximation of the sum-rate is derived. Particularly, the approximated sum-rate only relates to the user’s symbol SNR and the maximum of delay and Doppler shifts. More importantly, we propose a power allocation for different users based only on the statistical CSI to maximize the achievable sum-rate while ensuring user fairness. The optimal power allocation solution is obtained by a fractional programming approach. Our numerical results verify the derived PCRB and the sum-rate analysis, where a roughly 3 dB improvement in terms of channel estimation accuracy and a significant rate improvement can be obtained.

This paper proposes a novel reconfigurable intelligent surface (RIS)-aided integrated communication and positioning design for orthogonal frequency division multiplexing systems in indoor scenarios. A non-geometric strategy is employed to realize accurate positioning. Specifically, location-related information is embedded into channel frequency responses (CFR) and estimated through regular pilot subcarriers. The coefficients of RIS are optimized to maximize the norm of the CFR vector differences among users, exclusively considering physically adjacent users. To enhance positioning accuracy, we propose a two-stage framework that incorporates the prior information about the user in physical space. A unique feature, named “correlation dispersion”, within this framework is leveraged to enhance performance compared to geometric-based methods. By transforming the geometric prior information into the frequency domain capitalizing on Gaussian kernel method, we derive the Cramer-Rao Lower Bound (CRLB) of the proposed framework. A notable gain in CRLB is observed, highlighting the efficacy. Theoretical comparison with the CRLB of conventional methods validates the correlation dispersion property. Simulation results demonstrate a significant improvement in positioning accuracy when meticulously combining prior information with a non-geometric positioning method. Furthermore, our results unveil that the incorporation of rough positioning methods yields exceptionally high positioning performance, provided that the location information depicts different aspects.

The optical communication system using pulse position modulation (PPM) and superconducting nanowire single-photon detectors (SNSPDs) has attracted considerable attention for deep space applications as it enables high speed data transmission at extremely low average signal power. The deadtime of SNSPD is a critical factor in such systems because it primarily affects the signal detection efficiency. This becomes even more crucial in high-speed systems, where the deadtime can span several symbol periods. We employ the Markov chain model to characterize the high-speed PPM-SNSPD system and investigate its behavior. Analytical expressions for symbol transition probabilities are derived to characterize system-level metrics, including the symbol error rate and achievable code rate. Analysis shows that deadtime introduces memory to the PPM-SNSPD channel, resulting in channel asymmetry. Through experimental verification and simulations, we confirmed the effectiveness of our analysis. In addition, a set of new log-likelihood ratio (LLR) expressions is proposed based on the new model. Compared with the commonly used Poisson LLR expression, our proposed LLR expressions show more than 0.5 dB performance gain.

This paper investigates robust resource allocation design for active intelligent reflecting surface (IRS)-aided cognitive rate-splitting multiple access (C-RSMA) systems. In particular, an active IRS is deployed to shape a favorable wireless communication environment for enhancing the system performance. We aim to maximize the system energy efficiency by jointly optimizing the common rate allocations for the users, the transmit beamforming vectors at the coordinated base stations, and the active beamforming matrix at the IRS. We formulate the design as a non-convex optimization problem taking into account the discrete nature of the IRS elements and the transmit power budget constraints of the base stations as well as the active IRS. To tackle the non-convex design problem, a computationally effective iterative suboptimal algorithm is proposed by exploiting the block coordinate descent method, the generalized S-Procedure, the successive convex approximation, and the Dinkelbach’s approach. Simulation results reveal a non-trivial tradeoff between the system energy efficiency and the number of the IRS elements. Moreover, our results unveil that active IRS elements equipped with limited bit-resolution of discrete amplifiers and phase shifters is sufficient to achieve a significant gain in system energy efficiency.

Movable antennas (MAs) represent a promising paradigm to enhance the spatial degrees of freedom of conventional multi-antenna systems by dynamically adapting the positions of antenna elements within a designated transmit area. In particular, by employing electro-mechanical MA drivers such as stepper motors, the positions of the MA elements can be discretely adjusted to shape a favorable spatial correlation for improving system performance. Although preliminary research has explored beamforming designs for MA-enabled systems, the intricacies of the power consumption and the precise positioning of MA elements are not well understood, yet. Moreover, the assumption of perfect channel state information (CSI) adopted in the current literature is generally impractical due to the significant pilot overhead and the extensive time required for acquiring close-to-perfect CSI. To address these challenges, in this paper, we model the motion of MA elements through discrete steps and quantify the associated power consumption as a function of these movements. Furthermore, by leveraging the properties of the MA channel model, we introduce a novel CSI error model tailored for MA-enabled systems that facilitates robust resource allocation design. In particular, we jointly optimize the beamforming and the MA positions at the base station (BS) for minimization of the total BS power consumption, encompassing both radiated power and MA motion power, while guaranteeing a minimum required signal-to-interference-plus-noise ratio for each user. To this end, novel algorithms exploiting the branch and bound (BnB) method are developed to obtain the globally optimal solution for perfect and imperfect CSI, respectively. Moreover, to support practical real-time implementation, we propose low-complexity suboptimal algorithms with guaranteed convergence by leveraging successive convex approximation (SCA). Our numerical results validate the global optimality of the proposed BnB-based algorithms for both CSI scenarios. Furthermore, we unveil that both proposed SCA-based algorithms approach the optimal performance of the BnB-based algorithms within only a few iterations, thus highlighting their practical advantages. Additionally, we show that compared to the state-of-the-art approach, the proposed low-complexity SCA-based schemes achieve considerable performance gains, especially in high-load systems with a small number of antenna elements.

Hybrid digital-analog precoding is essential for balancing communication performance, energy efficiency, and hardware costs in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. However, most existing designs rely on the Shannon capacity and assume infinite blocklengths, which are impractical for emerging applications, such as massive machine-type communications, operating with finite blocklength (FBL). To address this gap, this paper pioneers a novel hybrid precoding design for mmWave massive MIMO in the FBL regime. We meticulously optimize hybrid precoding based on both the weighted sum-rate (WSR) and the max-min fairness (MMF) criteria, while fulfilling the transmit power budget and users’ minimum rate requirements. Both continuous and discrete phase shifters are considered for analog precoding. The formulated optimization problems are highly challenging to solve due to the nonconvex objective functions and nonconvex constraints. These challenges are further intensified by the nonconcave FBL rate function and the intricate coupling between analog and digital precoders. By proposing novel problem transformation and decomposition techniques, we reformulate the original complex problems into forms solvable with the penalty dual decomposition (PDD) method. We then develop two efficient iterative algorithms with parallel, and even closed-form variable updates, and guaranteed convergence to solve the WSR and MMF optimization problems, applicable to both continuous and discrete phase shifters. Simulation results show that our proposed hybrid precoding designs significantly outperform several baseline schemes, especially those adopting the Shannon capacity and infinite blocklength. Additionally, our proposed optimization algorithms enable hybrid precoding exploiting discrete phase shifters with limited quantization resolution (e.g., 3-bit) to closely match the performance of fully digital precoding in FBL scenarios.

The fifth-generation new radio (NR) cellular communication is featured with numerous advancements over Long Term Evolution (LTE) and earlier technologies. It enables more flexible physical-layer resource scheduling across multiple dimensions, and two representative techniques are beamspace transmissions and time-frequency numerology selection. Nevertheless, the lightweight physical-layer secure transmission in NR remains under investigation, especially taking NR beamspace and mobility into consideration. In this work, we propose a physical-layer wireless key generation (KG) efficient beamspace adaptation scheme for NR, where the KG capacity is theoretically characterized by critical NR components including beam direction and beamwidth. In addition, we consider the impacts of user mobility on KG performance. Since NR beamspace plays a key role in deciding the channel probing window in the spatial dimension, the NR beamspace directly affects channel probing results and hence the KG efficiency. To this end, NR beam parameters are obtained to improve the KG performance. Especially, we propose to optimize the NR beamwidth for maximizing the secrecy-delay efficiency, because a tradeoff exists in adapting the beamwidth where smaller beamwidth can improve the channel estimation accuracy but increase the beam sweeping delay. Theoretical analysis and simulation results show that the beam direction adaptation provides spatial degrees of freedom for NR to enhance KG, by enabling beam selection pointing at target areas with richer multipath scatterings. Experimental results demonstrate that the narrow beam is beneficial to enhancing the channel estimation accuracy and the resultant key agreements.

In this paper, we investigate the joint design of beamforming vectors and trajectory for unmanned aerial vehicles (UAVs) in integrated sensing and communications networks, aiming to maximize the achievable covert rate (ACR) for legitimate users against multiple passive wardens. Considering the worst-case scenario, where the wardens strategically select optimal decision thresholds, we derive the minimum detection error probability and incorporate covertness constraints within the beamforming scheme. Our approach entails formulating the design as a non-convex optimization problem for maximizing the average ACR along the UAV trajectory. The formulation takes into account various practical constraints, such as the maximum transmit power, UAV flight speed limitations, minimum beamforming gain towards sensing targets, and the detection probability threshold for wardens. To address this intricate problem, we propose a block coordinate descent-based optimization algorithm. This algorithm alternates between updating beamforming vectors and UAV trajectories, offering a high-quality suboptimal solution to the original problem. Theoretical analyses reveal that when the detection probability threshold is sufficiently small, a linear correlation emerges between the maximum relative variation ratio in the average received signal power at wardens under two hypotheses and the detection probability. Furthermore, to enhance covertness against the wardens, it is necessary to either decrease the projection of information beamforming covariance matrix or increase the projection of sensing beamforming covariance matrix onto the subspace spanned by the eavesdropping channel vectors. Finally, extensive simulations are presented to validate the covert performance enhancements of our proposed methodology, compared with various baseline schemes adopting existing approaches.

This paper investigates intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) visible light communication (VLC) networks utilizing the rate-splitting multiple access (RSMA) scheme. In these networks, an eavesdropper (Eve) attempts to eavesdrop on communications intended for legitimate users (LUs). To enhance information security and energy efficiency simultaneously, we formulate a secrecy energy efficiency (SEE) maximization problem by jointly optimizing the beamforming vectors, RSMA common rates, direct current (DC) bias, and IRS alignment matrices. The problem is constrained by total power budget, quality of service (QoS) requirements, linear operating region of light emitting diodes (LEDs), and common information rate allocation. Due to the non-convex and NP-hard nature of the formulated problem, we propose a deep reinforcement learning (DRL)-based dual-sampling proximal policy optimization (DS-PPO) approach. The approach leverages dual sample strategies and generalized advantage estimation (GAE). In addition, the maximum ratio transmission (MRT) and zero-forcing (ZF) are adopted to design the beamforming vectors. Simulation results show that the proposed DS-PPO approach outperforms traditional baseline approaches. Moreover, the implementation of the RSMA scheme and IRS contributes to overall system performance, achieving approximately 19.67% improvement over traditional multiple access schemes and 25.74% improvement over networks without IRS deployment.

To accommodate the proliferation of Internet-of-Things (IoT) applications, next-generation wireless communication networks, particularly the sixth-generation (6G), are expected to offer excellent support for the massive access of machine-type communication (MTC). In this paper, we investigate the grant-free random access (GFRA) employing orthogonal preambles in cell-free massive multiple-input multiple-output (mMIMO), which shows immense potential for enabling massive connectivity. In particular, we take into account the capture effect, defined as successful decoding despite preamble collisions, when the received signal-to-interference-plus-noise ratio (SINR) exceeds a predefined threshold. To this end, we develop an analytical framework to model GFRA with the capture effect adopting stochastic geometry. Subsequently, approximate analytical expressions for the received SINR and the access success probability for the typical GFRA frame structure are derived. Furthermore, leveraging these theoretical expressions, we formulate an optimization problem to determine the optimal preamble length that maximizes effective throughput. Simulation results validate the accuracy of our theoretical analyses and demonstrate the superior access performance of the optimized frame structure, whereas a frame structure with a constant preamble length does not consistently attain maximum effective throughput across varying user densities.

Extremely large-scale multiple-input multiple-output (XL-MIMO) is gaining attention as a prominent technology for enabling the sixth-generation (6G) wireless networks. However, the vast antenna array and the huge bandwidth introduce a non-negligible beam squint effect, causing beams of different frequencies to focus at different locations. One approach to cope with this is to employ true-time-delay lines (TTDs)-based beamforming to control the range and trajectory of near-field beam squint, known as the near-field controllable beam squint (CBS) effect. In this paper, we investigate the user localization in near-field wideband XL-MIMO systems under the beam squint effect and spatial non-stationary properties. Firstly, we derive the expressions for Cramér-Rao Bounds (CRBs) for characterizing the performance of estimating both angle and distance. This analysis aims to assess the potential of leveraging CBS for precise user localization. Secondly, a user localization scheme combining CBS and beam training is proposed. Specifically, we organize multiple subcarriers into groups, directing beams from different groups to distinct angles or distances through the CBS to obtain the estimates of users’ angles and distances. Furthermore, we design a user localization scheme based on a convolutional neural network model, namely ConvNeXt. This scheme utilizes the inputs and outputs of the CBS-based scheme to generate high-precision estimates of angle and distance. The numerical results derived from CRBs reveal that the inherent spatial non-stationary characteristics notably increase the CRB for angle, but have an insignificant impact on the CRB for distance estimation. In addition, the CRBs for both angle and distance decrease with increasing bandwidth and number of subcarriers. More importantly, our proposed ConvNeXt-based user localization scheme achieves centimeter-level accuracy in localization estimates.

This paper investigates asynchronous multiple-input multiple-output (MIMO) massive unsourced random access (URA) in an orthogonal frequency division multiplexing (OFDM) system over frequency-selective fading channels, with the presence of both timing and carrier frequency offsets (TO and CFO) and non-negligible codeword collisions. The proposed coding framework segregates the data into two components, namely, preamble and coding parts, with the former being tree-coded and the latter LDPC-coded. By leveraging the dual sparsity of the equivalent channel across both codeword and delay domains (CD and DD), we develop a message-passing-based sparse Bayesian learning algorithm, combined with belief propagation and mean field, to iteratively estimate DD channel responses, TO, and delay profiles. Furthermore, by jointly leveraging the observations among multiple slots, we establish a novel graph-based algorithm to iteratively separate the superimposed channels and compensate for the phase rotations. Additionally, the proposed algorithm is applied to the flat fading scenario to estimate both TO and CFO, where the channel and offset estimation is enhanced by leveraging the geometric characteristics of the signal constellation. Extensive simulations reveal that the proposed algorithm achieves superior performance and substantial complexity reduction in both channel and offset estimation compared to the codebook enlarging-based counterparts, and enhanced data recovery performances compared to state-of-the-art URA schemes.

This paper addresses the challenges of “double-fading” effect and coverage limitations encountered by passive intelligent reflecting surface (IRS) by introducing a novel IRS architecture, termed the dual-sided active-intelligent omni-surface (DSA-IOS). This architecture is capable of processing incident signals on both sides with controllable amplitudes and phases. Furthermore, the DSA-IOS is deployed in a multi-cell multiple-input single-output system to alleviate inter-cell interference and combat potential wiretapping from multi-antenna eavesdroppers. Considering eavesdroppers’ statistical channel state information, we introduce a system metric, the expected secrecy rate (ESR), to capture the tradeoff between secrecy rate (SR) and secrecy outage probability (SOP). Our objective is to maximize the system’s expected secrecy energy efficiency by jointly optimizing the beamformers and artificial noise at the base stations and the reflection and transmission coefficients for both sides at the DSA-IOS. To address the design problem, we propose a low-complexity alternating optimization scheme to acquire an effective suboptimal solution. Simulation results demonstrate that the proposed DSA-IOS outperforms other advanced IRS architectures in enhancing secure performance due to additional degrees of freedom for superior resource utilization. Our results also validate that the proposed ESR metric effectively balances the tradeoff between SR and SOP by customizing SOP thresholds for individual users.

To enhance the capacity and reliability of next-generation wireless communication systems, the novel cell-free massive multiple-input multiple-output (mMIMO) has emerged as a pivotal technology in satisfying the stringent quality of service requirements of massive network-connected devices. In this paper, we propose a partially coherent joint transmission (PCJT) approach that draws insights from both coherent and non-coherent joint transmission (NCJT) strategies. Specifically, we design the downlink transmit beamformers to maximize the weighted sum rate (WSR) and compare the performance in three distinct joint transmission modes, ranging from coherent and partially coherent, to non-coherent joint transmission. Specifically, a non-convex optimization problem is formulated that incorporates multiple data stream transmission and transmit power constraints. Given the intractability of the problem, the weighted minimum mean square error (WMMSE) approach is introduced to transform it into an equivalent form, which facilitates the development of a low-complexity and low-interaction reduced WMMSE (R-WMMSE) beamforming algorithm design to acquire an effective solution. For further reducing communication overhead and improving convergence rates, we propose a novel graph convolution network-based unfolding technique for R-WMMSE algorithm. It significantly reduces the number of iterations required while achieving similar performance to the original WMMSE algorithm, thus alleviating the signaling overhead burdens in distributive implementation. Simulation results demonstrate the significant performance gains achieved by the proposed algorithm in terms of superior WSR and rapid convergence performance. Furthermore, it is evident that the performance of PCJT can promote the performance achieved by NCJT, positioning it as an alternative between the existing two joint transmission strategies.

This paper investigates the sensing performance of two intelligent reflecting surface (IRS)-enabled non-line-of-sight (NLoS) sensing systems with fully- and semi-passive IRSs, respectively. In particular, we consider a fundamental setup with one base station (BS), one uniform linear array (ULA) IRS, and one point target in the NLoS region of the BS. Accordingly, we analyze both the sensing signal-to-noise ratio (SNR) and the Cramér-Rao bound (CRB) for estimating the target’s direction-of-arrival (DoA) with joint transmit and reflective beamforming optimization. First, we characterize the maximum sensing SNR when the BS-IRS channel follows line-of-sight (LoS) and Rayleigh fading, respectively. It is revealed that when the number of reflecting elementsNequipped at the IRS becomes sufficiently large, the maximum sensing SNR increases proportionally toN2andN4for the semi- and fully-passive IRSs, respectively. Then, we analyze the minimum CRB performance when the BS-IRS channel follows Rayleigh fading. It is shown that whenNgrows, the minimum CRB decreases inversely proportionally toN4andN6for the semi- and fully-passive IRS, respectively. Finally, numerical results are presented to corroborate our analysis across general channel conditions. It is shown that the fully-passive IRS outperforms the semi-passive counterpart whenNexceeds a certain threshold due to the additional reflective beamforming gain in the IRS-BS path, which efficiently compensates for the path loss.

This paper investigates resource allocation design for wideband integrated sensing and communication (ISAC) systems. To tackle the severe propagation attenuation issue in designing high-frequency ISAC systems, we adopt the hybrid beamformer at the transmitter to achieve substantial beamforming gains by generating highly directional beams. However, the well-known beam-split effect introduces multiple spatial directions at each subcarrier, due to the employment of wider bandwidth and a larger number of antennas, which may lead to system performance degradation. Fortunately, the notion of a true-time-delayer (TTD) has emerged as a crucial solution for compensating for the beam split by generating frequency-dependent phase shifts. To fully unleash its potential, we aim to minimize the Cramér-Rao Bound (CRB) for target estimation by jointly optimizing subcarrier allocation, digital beamforming matrices, and frequency-independent and frequency-dependent analog beamforming matrices at base station (BS). We formulate the optimization design as a non-convex mixed-integer non-linear programming (MINLP) problem, subject to the transmit power budget constraint of the BS, the rate quality-of-service (QoS) constraints for users, and the discrete nature of the analog beamformer. To achieve a globally optimal solution for the complex design problem, an iterative resource allocation algorithm is proposed by exploiting the generalized Bender’s decomposition (GBD) method. Moreover, we develop a computationally-efficient suboptimal algorithm to strike an effective balance between system performance and complexity. Our simulation results demonstrate the crucial importance of simultaneously optimizing all available degrees-of-freedom (DoFs) in wideband ISAC systems jointly and optimally. Furthermore, our proposed schemes are able to significantly improve the sensing accuracy over the traditional alternating optimization (AO) scheme adopted in existing solutions. Besides, our results unveil that deploying TTD units with limited bit-resolution time delays can achieve substantial gains in both communication and sensing performances.

Extremely large-scale multiple-input-multiple-output (XL-MIMO) is an emerging transceiver technology for enabling next-generation communication systems, due to its potential for substantial enhancement in both the spectral efficiency and spatial resolution. However, the achievable performance limits of various promising XL-MIMO configurations have yet to be fully evaluated, compared, and discussed. In this paper, we develop an effective degrees of freedom (EDoF) performance analysis framework specifically tailored for near-field XL-MIMO systems. We explore five representative distinct XL-MIMO hardware designs, including uniform planar array (UPA)-based with infinitely thin dipoles, two-dimensional (2D) continuous aperture (CAP) plane-based, UPA-based with patch antennas, uniform linear array (ULA)-based, and one-dimensional (1D) CAP line segment-based XL-MIMO systems. Our analysis encompasses two near-field channel models: the scalar and dyadic Green’s function-based channel models. More importantly, when applying the scalar Green’s function-based channel, we derive EDoF expressions in the closed-form, characterizing the impacts of the physical size of the transceiver, the transmitting distance, and the carrier frequency. In our numerical results, we evaluate and compare the EDoF performance across all examined XL-MIMO designs, confirming the accuracy of our proposed closed-form expressions. Furthermore, we observe that with an increasing number of antennas, the EDoF performance for both UPA-based and ULA-based systems approaches that of 2D CAP plane and 1D CAP line segment-based systems, respectively. Moreover, we unveil that the EDoF performance for near-field XL-MIMO systems is predominantly determined by the array aperture size rather than the sheer number of antennas.

Intelligent reflecting surfaces (IRSs) are a promising low-cost solution for achieving high spectral and energy efficiency in future communication systems by enabling the customization of wireless propagation environments. Despite the plethora of research on resource allocation design for IRS-assisted multiuser wireless communication systems, the optimal design and the corresponding performance upper bound are still not fully understood. To bridge this gap in knowledge, in this paper, we investigate the optimal resource allocation design for IRS-assisted multiuser multiple-input single-output (MISO) systems employing practical discrete IRS phase shifters. In particular, we jointly optimize the beamforming vector at the base station (BS) and the discrete IRS phase shifts to minimize the total transmit power for the cases of perfect and imperfect channel state information (CSI) knowledge. To this end, two novel algorithms based on the generalized Benders decomposition (GBD) method are developed to obtain the globally optimal solution for perfect and imperfect CSI, respectively. Moreover, to facilitate practical implementation, we propose two corresponding low-complexity suboptimal algorithms with guaranteed convergence by capitalizing on successive convex approximation (SCA). In particular, for imperfect CSI, we adopt a bounded error model to characterize the CSI uncertainty and propose a new transformation to convexify the robust quality-of-service (QoS) constraints. Our numerical results confirm the optimality of the proposed GBD-based algorithms for the considered system for both perfect and imperfect CSI. Furthermore, we unveil that both proposed SCA-based algorithms can attain a locally optimal solution within a few iterations. Moreover, compared with the state-of-the-art solution based on alternating optimization (AO), the proposed low-complexity SCA-based schemes achieve a significant performance gain, especially for moderate-to-large numbers of IRS elements.

Orthogonal time frequency space (OTFS) modulation is anticipated to be a promising candidate for supporting integrated sensing and communications (ISAC) systems, which is considered as a pivotal technique for realizing next-generation wireless networks. In this paper, we develop a minimum bit error rate (BER) precoder design for an OTFS-based ISAC system. In particular, the BER minimization problem takes into account the maximum available transmission power budget and the required sensing performance. Unlike previous studies that focused on ISAC in the time-frequency (TF) domain, we devise the precoder from the perspective of the delay-Doppler (DD) domain by exploiting the equivalent DD domain channel. The DD domain channel generally tends to be sparse and quasi-static, which is conducive to a low-complexity ISAC system design. To address the non-convex optimization design problem, we resort to optimizing the lower bound of the derived average BER by adopting Jensen’s inequality. Subsequently, the formulated problem is decoupled into two independent sub-problems via singular value decomposition (SVD) methodology. We then theoretically analyze the feasibility conditions of the proposed problem and present a low-complexity iterative solution via leveraging the Lagrangian duality approach. Simulation results verify the effectiveness of our proposed precoder compared to the benchmark schemes and reveal the interplay between sensing and communication for dual-functional precoder design, indicating a trade-off where transmission efficiency is sacrificed for increasing transmission reliability and sensing accuracy.

For integrated sensing and communication (ISAC) systems, channel information that is essential for communication and sensing tasks fluctuates at different timescales. Specifically, the composite channel state information (CSI) for wireless communication is static during channel coherence time. However, this concept is less appropriate for describing the wireless channel for sensing. To this end, in this paper, we first introduce a new timescale to study the real-time variations of the path state information (PSI) (e.g., delay, angle, and Doppler) of individual multi-path, termed path-invariant time, during which the PSI remains constant. As the goal of environment sensing for PSI essentially aligns with the channel estimation for the recently proposed delay-Doppler alignment modulation (DDAM) technique, we introduce a novel framework for a bi-static ISAC system, which refers to as DDAM-based ISAC. To acquire the PSI, in this paper, by capitalizing on the dual timescales of wireless channels, we propose a novel algorithm, termed as adaptive simultaneously orthogonal matching pursuit algorithm with support refinement (ASOMP-SR). The performance of DDAM with the imperfectly sensed PSI is analyzed, where the signal-to-interference-plus-noise ratio (SINR) and the achievable spectral efficiency are derived. Numerical results unveil that the proposed ASOMP-SR algorithm achieves better sensing performance than the conventional orthogonal matching pursuit (OMP) algorithm, in terms of the normalized mean squared error (NMSE) and the number of multi-paths resolved. In addition, DDAM-based ISAC can achieve superior spectral efficiency and a reduced peak-to-average power ratio (PAPR) compared to standard orthogonal frequency division multiplexing (OFDM).

In the era of upcoming sixth-generation (6G) wireless systems, the intelligent integrated sensing and communication (ISAC) paradigm has emerged as a pivotal research domain, catalyzing advancement across a wide range of applications. In this paper, we investigate an ISAC-assisted anti-eavesdropping communication system, where an ISAC ground base station exploits its radar function to track potential aerial eavesdroppers and implements predictive beamforming to ensure secure communications with multiple ground users. We harness the powerful capability of the Transformer for time series prediction to establish a novel deep neural network, termed the ISACformer, for constructing predictive beamformers via exploiting previously estimated channel state information in an unsupervised manner. By eliminating the need for explicit channel prediction, our proposed framework effectively reduces signaling overhead and complexity. In addition, by formulating a weighted objective function, our design meticulously balances the trade-off between the ergodic achievable worst-case secrecy rate for ground users and the ergodic Cramér-Rao lower bound for the kinematic parameters of potential aerial eavesdroppers. Simulation results demonstrate that the proposed ISACformer can deliver the desired predictive beamforming for harmonizing radar and communication functionalities effectively. Moreover, our method achieves performance approaching the theoretical upper bound obtained by ignoring multi-user interference, thereby highlighting the robustness of the proposed approach.

Integrated sensing and communication (ISAC) can enhance spectral efficiency and facilitate the diverse emerging applications via sharing the same spectrum and hardware between communication and sensing. However, effective operation of ISAC may suffer from high complexity. In this paper, we develop a low-complexity deep unfolding learning-aided transceiver design scheme for ISAC in a cluttered environment. In particular, we optimize the transmit waveform and receive filtering to minimize the weighted sum of multi-user interference power and the reciprocal of sensing signal-to-interference-plus-noise ratio (SINR), while adhering to the constraints of a constant modulus signal and waveform similarity. An alternating direction method of multipliers (ADMM)-based iterative algorithm is first developed to address this non-convex optimization problem with both equality and inequality constraints. To further reduce the computational complexity, we develop two deep unfolding neural networks (NNs), termed ADMM-DL-NET and ADMM-PGD-NET, to handle this problem, which can unfold the underlying ADMM-based iterative algorithm to a lightweight neural network with learnable parameters and eliminate the need for the bisection method by adopting the Uzawa’s method and projected gradient descent, respectively. Simulation results demonstrate that our proposed deep unfolding NNs can achieve comparable performance to the ADMM-based iterative algorithm with significantly reduced complexity, and outperform the unsupervised learning benchmarks in performance and number of learnable parameters.

Despite the enhanced spectral efficiency brought by the integrated radar and communication technique, it poses significant risks to communication security when confronted with malicious radar targets. To address this issue, a reconfigurable intelligent surface (RIS)-aided transmission scheme is proposed to improve secure communication in two systems, i.e., the radar and communication co-existing (RCCE) system, where a single transmitter is utilized for both radar sensing and communication, and the dual-functional radar and communication (DFRC) system. At the design stage, optimization problems are formulated to maximize the secrecy rate while satisfying the radar detection constraint via joint active beamforming at the base station and passive beamforming of RIS in both systems. Particularly, a zero-forcing-based block coordinate descent (BCD) algorithm is developed for the RCCE system. Besides, the Dinkelbach method combined with semidefinite relaxation is employed for the DFRC system, and to further reduce the computational complexity, a Riemannian conjugate gradient-based alternating optimization algorithm is proposed. Moreover, the RIS-aided robust secure communication in the DFRC system is investigated by considering the eavesdropper’s imperfect channel state information (CSI), where a bounded uncertainty model is adopted to capture the angle error and fading channel error of the eavesdropper, and a tractable bound for their joint uncertainty is derived. Simulation results confirm the effectiveness of the developed RIS-aided transmission scheme to improve the secrecy rate even with the eavesdropper’s imperfect CSI, and comparisons between both systems reveal that the RCCE system can provide a higher secrecy rate than the DFRC system.

In this paper, we investigate the near-field (NF) channel estimation (CE) for extremely large-scale multiple-input multiple-output (XL-MIMO) systems. Considering the pronounced NF effects in XL-MIMO communications, we first establish a joint angle-distance (AD) domain-based spherical-wavefront physical channel model that captures the inherent sparsity of XL-MIMO channels in the NF region. Leveraging the sparsity of the channel, the CE is approached as a task of reconstructing sparse signals. Anchored in this framework, we first propose a compressed sensing algorithm to acquire a preliminary channel estimation. Harnessing the powerful latent representation capability of generative artificial intelligence (GenAI), we further propose a GenAI-based approach to refine the estimated channel by employing advanced image denoising techniques. Specifically, we perceive the estimated channel as a noisy color image. Then, we derive the evidence lower bound (ELBO) of the design objective utilizing variational inference and reparameterization techniques, and propose a generative diffusion probabilistic model (GDM) dedicated to denoising. Experimental results indicate that the proposed GDM is capable of offering substantial performance gain in CE compared to existing benchmark approaches in NF XL-MIMO systems.

This paper investigates the cooperative integrated communication and positioning (ICAP) within multiple-input single-output (MISO) systems. A novel strategy that leverages positioning outcomes is presented to enhance the quality of imperfect channel state information (ICSI), thereby bridging these two functionalities. Initially, a fingerprint-based method is developed to establish positioning function with the channel frequency responses serving as identification features. We focus on the interplay of geometric and non-geometric spatial consistency to improve positioning accuracy. To this end, reconfigurable intelligent surface (RIS) and transmit precoding techniques are employed to promote the non-geometric spatial consistency while ensuring communication quality. Moreover, we refine the distribution of random channel uncertainty through the Bayes pooling principle by leveraging the reshaped spatial consistency, resulting in an updated ICSI covariance matrix. This refinement is proven to significantly enhance the quality of ICSI, as evidenced by a reduction in the minimum mean square error. Furthermore, the study introduces a progressive transmission protocol that reduces training overhead in line with the channel enhancement strategy. Numerical results validate that the proposed protocol enhances the accuracy positioning by adjusting the power allocation without compromising the performance of communication. Moreover, opting for overhead reduction rather than directly utilizing enhanced ICSI quality demonstrates superior communication performance.

In this work, we consider an intelligent reflecting surface (IRS)-assisted covert wireless communication system, in which the location of a warden Willie follows a binomial point process (BPP) distribution within a disk centered at O with a radius of R and the warden is outside a safety zone centered at C with a radius of r. The location of the safety zone can be anywhere (inside, outside, or having intersections) relative to the disk. We first analyze the detection performance of Willie for the cases with known channel state information (CSI) and unknown CSI, respectively, based on which we determine Willie's minimum detection error rate. Then, considering the geometric randomness of Willie's position inside the disk but outside the safety zone, the average minimum detection error rate is determined and adopted in the covertness constraint in the subsequent covert system design. Our examination first shows that the covert communication rate generally increases with R. However, more interestingly, whether the covert communication rate increases or decreases with r generally depends on the relative locations of the disk, the safety zone, and Alice. For example, when both the safety zone and Alice are on the same side of the disk center O, the covert rate increases with r, and vice versa.

This paper investigates the bit error rate (BER) minimum precoder design for an orthogonal time frequency space (OTFS)-based integrated sensing and communications (ISAC) system, which is considered as a promising technique for enabling future wireless networks. In particular, the BER minimum problem takes into account the maximized available transmission power and the required sensing performance. We devise the precoder from the perspective of delay-Doppler (DD) domain by exploiting the equivalent DD channel. To address the non-convex design problem, we resort to minimizing the lower bound of the derived average BER. Afterwards, we propose a computationally iterative method to solve the dual problem at low cost. Simulation results verify the effectiveness of our proposed precoder and reveal the interplay between sensing and communication for dual-functional precoder design.

Employing unmanned aerial vehicles (UAVs) for data collection is crucial in facilitating autonomous monitoring applications within wireless sensor networks (WSNs). To enable sustainable WSNs, wireless powering of ground nodes (GNs) from a flying UAV is a promising technique. However, to maximize utility, we need to smartly allocate the limited resources of UAVs. To this end, we propose jointly optimizing the UAV’s trajectory and time allocation per GN to reduce operation time. We first formulate a non-convex optimization problem for data collection that minimizes operation time while satisfying the sum throughput and time constraint. Thereafter, we develop a methodology that decouples the original problem into two sub-problems: time allocation and trajectory planning. Here, the former is solved in semi-closed form, while a genetic algorithm is employed to solve the latter. Simulations confirm the efficiency of our proposed model and unveil an up to 30% improvement in operation time compared to the existing benchmarks.

In this paper, to address the “double fading” effect and coverage limitations encountered by conventional passive intelligent reflecting surface (IRS), we propose a novel IRS hardware architecture, termed the dual-sided active (DSA)-IRS, which is capable of simultaneously processing dual-sided incident signals with controllable both amplitude and phase. Furthermore, the DSA-IRS is deployed in a multi-cell multiple-input single-output (MISO) system to alleviate inter-cell interference. Our design objective is to maximize the weighted sum-rate (WSR) among all users by jointly optimizing the beamformers at the base stations (BSs) and the reflection and transmission coefficients for both sides at the DSA-IRS, which is formulated as a non-convex optimization problem. To address the problem, we propose an alternating optimization (AO) scheme to obtain an effective suboptimal solution. Simulation results demonstrate that the proposed DSA-IRS outperforms other advanced IRS architectures in enhancing system performance in multi-cell communications due to the additional degrees of freedom for superior resource utilization.

Stacked intelligent metasurfaces (SIM) represents an advanced signal processing paradigm that enables over-the-air processing of electromagnetic waves at the speed of light. Its multi-layer structure exhibits customizable increased computational capability compared to conventional single-layer reconfigurable intelligent surfaces and metasurface lenses. In this paper, we deploy SIM to improve the performance of multi-user multiple-input single-output (MISO) wireless systems with low complexity transmit radio frequency (RF) chains. In particular, an optimization formulation for the joint design of the SIM phase shifts and the transmit power allocation is presented, which is efficiently solved via a customized deep reinforcement learning (DRL) approach that continuously observes pre-designed states of the SIM-parametrized smart wireless environment. The presented performance evaluation results showcase the proposed method's capability to effectively learn from the wireless environment while outperforming conventional precoding schemes under low transmit power conditions. Finally, a whitening process is presented to further augment the robustness of the proposed scheme.

Future sixth-generation (6G) systems are expected to leverage extremely large-scale multiple-input multiple-output (XL-MIMO) technology, which significantly expands the range of the near-field region. While accurate channel estimation is essential for beamforming and data detection, the unique characteristics of near-field channels pose additional challenges to the effective acquisition of channel state information. In this paper, we propose a novel codebook design, which allows efficient near-field channel estimation with significantly reduced codebook size. Specifically, we consider the eigen-problem based on the near-field electromagnetic wave transmission model. Moreover, we derive the general form of the eigenvectors associated with the near-field channel matrix, revealing their noteworthy connection to the discrete prolate spheroidal sequence (DPSS). Based on the proposed near-field codebook design, we further introduce a two-step channel estimation scheme. Simulation results demonstrate that the proposed codebook design not only achieves superior sparsification performance of near-field channels with a lower leakage effect, but also significantly improves the accuracy in compressive sensing channel estimation.

Edge intelligence is expected to revolutionize the industrial Internet of Things (IoT) by providing proximal intelligent services to massive low-cost IoT devices. However, integration of the two paradigms needs to simultaneously maximize the edge quality of training (QoT) and IoT quality of service (QoS) under time-varying co-channel interference, for which the existing edge or IoT resource allocation algorithms become ineffective, as they ignore the contradiction between learning performance and communication requirements. This paper proposes an edge intelligence industrial IoT (EI3) framework, which jointly maximizes QoT and QoS through a newly derived learning-communication balancing power allocation (LCBPA) formulation. An efficient algorithm is proposed to solve the non-convex and non-smooth LCBPA problem. Simulation results demonstrate that the proposed LCBPA scheme achieves superior performance compared to several benchmarks in terms of the desired learning accuracy and qualified transmission rate. It is also shown that EI3can adapt to new scenarios by flexibly adjusting the importance factor between learning and communication.

Resource allocation is paramount to improve spectral efficiency in spectrum-sharing networks. However, numerous existing resource allocation schemes, especially those based on deep reinforcement learning, overlook the impact of time-variant channel quality caused by high dynamics of wireless environment, resulting in limited performance. To tackle this issue, an intelligent resource allocation scheme, enhanced by channel prediction, is proposed to jointly optimize channel allocation and transmission power. A multiple-channel prediction network utilizing the gated recurrent unit is designed to learn the evolutionary characteristics of time-varying channels. Meanwhile, an intelligent framework is proposed to capitalize fully on channel quality variations for resource allocation. Simulation results demonstrate that our proposed scheme achieves superior performance compared with other benchmark schemes, highlighting that the sum transmission rate can be improved by exploiting channel characteristics.

Autonomous cooperative planning (ACP) is a promising technique to improve the efficiency and safety of multi-vehicle interactions for future intelligent transportation systems. However, realizing robust ACP is a challenge due to the aggregation of perception, motion, and communication uncertainties. This paper proposes a novel multi-uncertainty aware ACP (MUACP) framework that simultaneously accounts for multiple types of uncertainties via regularized cooperative model predictive control (RC-MPC). The regularizers and constraints for perception, motion, and communication are constructed according to the confidence levels, weather conditions, and outage probabilities, respectively. The effectiveness of the proposed method is evaluated in the Car Learning to Act (CARLA) simulation platform. Results demonstrate that the proposed MUACP efficiently performs cooperative formation in real time and outperforms other benchmark approaches in various scenarios under imperfect knowledge of the environment.

In this paper, we investigate the performance of the cross-domain iterative detection (CDID) framework with orthogonal time frequency space (OTFS) modulation, where two distinct CDID algorithms are presented. The proposed schemes estimate/detect the information symbols iteratively across the frequency domain and the delay-Doppler (DD) domain via passing either the a posteriori or extrinsic information. Building upon this framework, we investigate the error performance by considering the bias evolution and state evolution. Furthermore, we discuss their error performance in convergence and the DD domain error state lower bounds in each iteration. Specifically, we demonstrate that in convergence, the ultimate error performance of the CDID passing the a posteriori information can be characterized by two potential convergence points. In contrast, the ultimate error performance of the CDID passing the extrinsic information has only one convergence point, which, interestingly, aligns with the matched filter bound. Our numerical results confirm our analytical findings and unveil the promising error performance achieved by the proposed designs.

Federated learning (FL) over wireless networks offers a promising approach to enable decentralized machine learning among massive mobile edge nodes while ensuring privacy in training data. However, the convergence speed of FL is limited by the straggler effect, which arises from heterogeneous nodes, wireless fading channels, and non-independently and identically distributed (non-IID) training data. In this paper, we consider an adaptive semi-asynchronous FL to mitigate the straggler effect, by dynamically selecting subsets of nodes over time to synchronize the global model. We jointly optimize the node scheduling and computing/communication resource allocation to minimize the completion time required for convergence of the adaptive semi-asynchronous FL. Leveraging the convergence condition of semi-asynchronous FL, we further propose a greedy heuristic policy for node scheduling while tackling the remaining computing/communication resource allocation problem by exploiting a hidden convexity. Simulation results on open datasets demonstrate that, compared with existing FL algorithms, our proposed adaptive semi-asynchronous algorithm can significantly lower the latency of FL convergence.

Integrated sensing and communication (ISAC) technology effectively enables spectrum and hardware sharing between radar and communication. This paper investigates the dual-functional (DF) constant modulus waveform design for simultaneously transmitting and reconfigurable intelligent surface (STAR-RIS)-aided ISAC, in which the channel information can be used as semantic information. To investigate the performance trade-off, the weighted sum of multi-user interference (MUI) energy and waveform discrepancies is minimized via jointly optimizing the transmit waveform and the reflection and transmission coefficient matrices at STAR-RIS. Furthermore, a practical case of coupled phase shifts at STARRIS is investigated. We first formulate the optimization problem as a Markov decision process, employing a twin delayed deep deterministic policy gradient (TD3)-based deep reinforcement learning approach to address it. Simulation results verify the effectiveness of the proposed scheme.

In this paper, we study the parameter estimation performance for monostatic downlink integrated sensing and communications (ISAC) systems. In particular, we analyze the mean squared error (MSE) lower bound for target sensing in the downlink ISAC system that reveals the suboptimality in re-using the conventional communication waveform for sensing. To realize a practical dual-functional waveform, we propose a waveform augmentation strategy that imposes an extra signal structure, namely the degrees-of-freedom (DoF) completion method. The proposed approach is capable of improving the parameter estimation performance of the ISAC system and achieving the derived MSE lower bound. To improve the performance of the proposed strategy, we formulate an MSE minimization problem to design the ISAC precoder, subject to the communication users' signal-interference-plus-noise-ratio (SINR) constraints. Despite the non-convexity of the waveform design problem, we obtain its globally optimal solution via semi-definite relaxation (SDR) and the proposed constructive method. Simulation results validate the proposed DoF completion technology could achieve the derived MSE lower bound and the effectiveness of the MSE-based ISAC waveform design.

This paper investigates robust resource allocation design for active intelligent reflecting surface (IRS)-aided cog-nitive rate-splitting multiple access (C-RSMA) systems. In particular, an active IRS is deployed to shape a favorable wireless communication environment for enhancing the system performance. We aim to maximize the system energy efficiency by jointly optimizing the common rate allocations for the users, the transmit beamforming vectors at the coordinated base stations, and the active beamforming matrix at the IRS. We formulate the resource allocation design as a non-convex optimization problem taking into account the discrete nature of the IRS elements and the transmit power budget constraints of the base stations as well as the active IRS. To tackle the non-convex design problem, we propose a computationally effective iterative suboptimal algorithm. Simulation results reveal a nontrivial tradeoff between the system energy efficiency and the number of the IRS elements. Moreover, our results unveil that active IRS elements equipped with limited bit-resolution of discrete amplifiers and phase shifters is sufficient to achieve a significant gain in the system energy efficiency.

The envisioned sixth-generation (6G) of wireless communications is expected to give rise to the necessity of connecting very large quantities of heterogeneous wireless devices, which requires advanced system capabilities far beyond existing network architectures. In particular, such massive communication has been recognized as a prime driver that can empower the 6G vision of future ubiquitous connectivity, supporting Internet of Human-Machine-Things (IoHMT) for which massive access is critical. This article surveys the most recent advances toward massive access in both academic and industrial communities, focusing primarily on the promising compressive sensing (CS)-based grant-free massive access (GFMA) paradigm. We first specify the limitations of existing random access schemes and reveal that the practical implementation of massive communication relies on a dramatically different random access paradigm from the current ones mainly designed for human-centric communications. Then, a CS-based GFMA roadmap is presented, where the evolutions from single-antenna to large-scale antenna array-based base stations, from single-station to cooperative massive multiple-input-multiple-output (MIMO) systems, and from unsourced to sourced random access scenarios are detailed. Finally, we discuss key challenges and open issues to indicate potential future research directions in GFMA.

This article investigates rate-splitting multiple access (RSMA)-based visible light communication (VLC) networks with simultaneous lightwave information and power transfer (SLIPT). To effectively enhance the fairness among information decoding users (IDUs), we formulate an optimization problem to maximize the minimum data rate by optimizing the direct current bias vector, the common message rates of RSMA, and the transmit precoding vectors. In the problem, the IDUs’ minimum energy harvesting (EH) requirements, the total power budget of the light-emitting diode (LED) transmitters, and the linear operation region of LEDs are also considered as the system constrains. To solve the formulated nonconvex problem, epigraph reformulation is first employed to transform the nonconvex objective function. Then, a series of transformations is proposed and the semi-definite relaxation (SDR) method is adopted to address the rank-one precoding matrix constraint. After that, an iterative algorithm is proposed to obtain an effective suboptimal solution by applying the successive convex approximation. Extensive simulations show that the max–min rate (MMR) is inversely proportional to the number of IDUs and it decreases as the minimum EH requirement becomes more stringent, especially in the high-EH region. Moreover, the value of the maximum drive current imposes a significant impact on the system performance, particularly, the MMR becomes saturated for a given maximum drive current even if the total power budget is sufficient. Besides, RSMA can contribute to both spectral efficiency and energy efficiency greatly in comparison to the traditional multiple access scheme.

Integrated sensing and communication (ISAC) is envisioned as a key pillar for enabling the upcoming sixth generation (6G) communication systems, requiring not only reliable communication functionalities but also highly accurate environmental sensing capabilities. In this paper, we design a novel networked ISAC framework to explore the collaboration among multiple users for environmental sensing. Specifically, multiple users can serve as powerful sensors, capturing back scattered signals from a target at various angles to facilitate reliable computational imaging. Centralized sensing approaches are extremely sensitive to the capability of the leader node because it requires the leader node to process the signals sent by all the users. To this end, we propose a two-step distributed cooperative sensing algorithm that allows low-dimensional intermediate estimate exchange among neighboring users, thus eliminating the reliance on the centralized leader node and improving the robustness of sensing. This way, multiple users can cooperatively sense a target by exploiting the block-wise environment sparsity and the interference cancellation technique. Furthermore, we analyze the mean square error of the proposed distributed algorithm as a networked sensing performance metric and propose a beamforming design for the proposed network ISAC scheme to maximize the networked sensing accuracy and communication performance subject to a transmit power constraint. Simulation results validate the effectiveness of the proposed algorithm compared with the state-of-the-art algorithms.

This paper studies a secure cell-free integrated sensing and communication (ISAC) system, in which multiple ISAC transmitters collaboratively send confidential information to multiple communication users (CUs) and concurrently conduct target detection. Different from prior works investigating communication security against potential information eavesdropping, we consider the security of both communication and sensing in the presence of information and sensing eavesdroppers that aim to intercept confidential communication information and extract target information, respectively. Towards this end, we optimize the joint information and sensing transmit beamforming at these ISAC transmitters for secure cell-free ISAC. Our objective is to maximize the detection probability over a designated sensing area while ensuring the minimum signal-to-interference-plus-noise-ratio (SINR) requirements at CUs. Our formulation also takes into account the maximum tolerable signal-to-noise ratio (SNR) constraints at information eavesdroppers for ensuring the confidentiality of information transmission, and the maximum detection probability constraints at sensing eavesdroppers for preserving sensing privacy. The formulated secure joint transmit beamforming problem is highly non-convex due to the intricate interplay between the detection probabilities, beamforming vectors, and SINR constraints. Fortunately, through strategic manipulation and via applying the semidefinite relaxation (SDR) technique, we successfully obtain the globally optimal solution to the design problem by rigorously verifying the tightness of SDR. Furthermore, we present two alternative joint beamforming designs based on the sensing SNR maximization over the specific sensing area and the coordinated beamforming, respectively. Numerical results reveal the benefits of our proposed design over these alternative benchmarks.

Mobile edge computing-assisted integrated aerial-ground network (MEC-IAGN) emerges as a promising key component of the sixth-generation (6G) wireless networks due to its potential capabilities in providing ubiquitous connectivity for global coverage and computing services. However, the inevitable existences of computation-intensive tasks, uncontrollable propagation environment, and malicious jamming attacks pose three significant bottlenecks for enabling efficient MEC-IAGN. With these focuses, we propose a novel framework of multi-functional reconfigurable intelligent surface (MF-RIS) aided semantic anti-jamming communication and computing in MEC-IAGN. Under this framework, a semantic transceiver exhibits inherent robustness and data compression capability, and MF-RIS can customize the full-space wireless environment by leveraging its signal reflection, refraction, amplification, and energy harvesting functions, thereby achieving substantial global coverage, reliable connectivity, and high-rate computing. Based on our proposed framework, we formulate a semantic computation rate maximization problem considering the impacts of jammer’s channel state information (CSI) imperfection, while maintaining the energy partition constraint for computation offloading decision, semantic similarity requirement, semantic computation rate target, and MF-RIS’s self-sustainability. Then, by transforming the imperfect CSI into a worst-case one by exploiting a discretization method, we propose a fast-converging monotonic optimization algorithm that is combined with decoupling second-order cone programming to obtain a globally optimal solution with fewer feasibility evaluations. Furthermore, to strike a satisfactory tradeoff between performance and computational complexity, we develop a suboptimal generalized power iteration algorithm. Numerical simulations demonstrate the superiority of our proposed framework and algorithms compared to various benchmarks.

The formation control of multi-agent systems has increasingly drawn attention for fulfilling numerous emerging applications and services. To achieve high-accuracy formation, the location awareness of all agents becomes an essential requirement. In this paper, we address the problem of network localization and formation control in a cooperative system with asynchronous agents. In particular, we formulate the joint localization and synchronization of agents as a statistical inference problem. The underlying probabilistic model is represented by a factor graph from which a message-passing algorithm is designed that computes approximations of the marginals of unknown variables, i.e. agents’ locations and clock offsets. Due to the Euclidean-norm operator involved in their computation no parametric closed-form expressions of the messages exist. As a compromise, implemented message-passing methods therefore resort to approximations of these messages. Conventional methods rely either on a first-order Taylor expansion of the norm operation or on non-parametric representations, e.g. by means particle filters (PFs), to compute such approximations. However, the former approach suffers from poor performance while the latter one experiences high complexity. The proposed message-passing algorithm in this paper is parametric. Specifically, it passes Gaussian messages that can be essentially obtained by suitably augmenting the factor graph and applying on it a hybrid method for combining belief propagation and variational message passing. Subsequently, the agents can exploit the estimated locations for determining the control policy. Two types of control policy are designed based on the optimization of a generalized cost function. We show that the proposed scheme enjoys a reduced complexity for multi-agent localization while achieving the desired formation with excellent accuracy.

Multiple access is the cornerstone technology for each generation of wireless cellular networks, which fundamentally determines the method of radio resource sharing and significantly influences both the system performance and transceiver complexity. Meanwhile, resource allocation (RA) design plays a crucial role in multiple access, as it can manage both encompassing radio resources and interference, and it is critical for providing high-speed and reliable communication services to multiple users. Given that the RA design is intrinsically scenario-specific and the optimization tools for RA design are typically varied, in this article, we present a comprehensive tutorial overview for junior researchers in this field, aiming to offer a foundational guide for RA design in the context of next-generation multiple access (NGMA). Our discussion spans a broad range of fundamental topics: from typical system models, through intriguing problem formulation in RA design, to the exploration of various potential optimization solution methodologies. Initially, we identify three types of channels in future wireless cellular networks over which NGMA will be implemented, namely, natural channels, reconfigurable channels, and functional channels. Natural channels are traditional uplink and downlink communication channels; reconfigurable channels are defined as channels that can be proactively reshaped via emerging platforms or techniques, such as intelligent reflecting surface (IRS), unmanned aerial vehicle (UAV), and movable/fluid antenna (M/FA); and functional channels support not only communication but also other functionalities simultaneously, with typical examples, including integrated sensing and communication (ISAC) and joint computing and communication (JCAC) channels. Then, we introduce NGMA models applicable to these three types of channels that cover most of the practical communication scenarios of future wireless communications. Subsequently, we articulate the key optimization technical challenges inherent in the RA design for NGMA, categorizing them into rate-, power-, and reliability-oriented RA designs. The corresponding optimization approaches for solving the formulated RA design problems are then presented. Finally, the simulation results are presented and discussed to elucidate the practical implications and insights derived from RA designs in NGMA.

Channel state information (CSI) feedback is critical for achieving the promised advantages of enhancing spectral and energy efficiencies in massive multiple-input multiple-output (MIMO) wireless communication systems. Deep learning (DL)-based methods have been proven effective in reducing the required signaling overhead for CSI feedback. In practical dual-polarized MIMO scenarios, channels in the vertical and horizontal polarization directions tend to exhibit high polarization correlation. To fully exploit the inherent propagation similarity within dual-polarized channels, we propose a disentangled representation neural network (NN) for CSI feedback, referred to as DiReNet. The proposed DiReNet disentangles dual-polarized CSI into three components: polarization-shared information, vertical polarization-specific information, and horizontal polarization-specific information. This disentanglement of dual-polarized CSI enables the minimization of information redundancy caused by the polarization correlation and improves the performance of CSI compression and recovery. Additionally, flexible quantization and network extension schemes are designed. Consequently, our method provides a pragmatic solution for CSI feedback to harness the physical MIMO polarization as a priori information. Our experimental results show that the performance of our proposed DiReNet surpasses that of existing DL-based networks, while also effectively reducing the number of network parameters by nearly one third.

Cell-free (CF) networks are a promising architecture poised to revolutionize future wireless communication systems. To enhance performance, designing effective transmission strategies for CF networks is of practical significance. In this paper, we study the downlink beamforming design to maximize the resource efficiency (RE) of CF networks, which encompasses both energy efficiency (EE) and spectral efficiency (SE) optimization, thereby enabling the realization of an EE-SE tradeoff. Specifically, we formulate a RE maximization problem taking into account both the quality of service (QoS) requirements of the users and the power constraint of the network. The RE optimization problem is a non-convex fractional program. To solve it efficiently, we first equivalently decompose the challenging RE problem into two more tractable problems, a subproblem and a primary problem. Then, we show that the subproblem is equivalent to a power minimization problem and propose two effective methods to obtain the optimal primal and dual solutions simultaneously. After that, we derive the gradient of the optimal value function of the subproblem exploiting the obtained primal and dual solutions that facilitates the design of efficient algorithms with rapid convergence to address the primary problem. Finally, numerical results demonstrate that the proposed algorithms can effectively balance the EE-SE tradeoff, surpassing existing approaches in terms of either EE or SE.

Reconfigurable intelligent surface (RIS) has the capability to significantly enhance physical layer security by reconfiguring the propagation in wireless communications. However, due to the cascaded channel brought by the RIS and the hostile nature of potential eavesdroppers, acquiring perfect channel state information (CSI) of the eavesdroppers is challenging. Worse still, if the eavesdroppers are equipped with multiple antennas and there exists spatial correlation at the RIS due to closely spaced RIS elements, the random channel matrices are complicatedly coupled with the phase shift and other wireless resources in the outage probabilistic constraint, making their optimizations intractable. To date, there has been no systematic and feasible approach to address such a challenge. To fill this gap, this paper for the first time reveals an analytical transformation for handling the intractable outage probabilistic constraint. It is theoretically established that when the maximum tolerable outage probability is smaller than a threshold around 0.4, which generally holds in practice, the proposed transformation is exact and suffers no performance loss. As an illustrative example of the developed constraint transformation, the secure energy efficiency maximization is selected as the objecitve function and the resultant resource optimization is handled by the alternating maximization framework. Numerical results are presented to show the rapid convergence behavior of the proposed algorithm and unveil that the proposed probabilistic constraint transformation has superiority over the Bernstein-Type Inequality approximation. Compared with several baseline schemes (e.g., random phase-shift, fixed phase-shift, RIS ignoring CSI uncertainty, and secure transmission without RIS), the proposed scheme significantly boosts the performance, underscoring the significance of appropriately managing the probabilistic constraint outage and optimizing RIS phase shifts for secure transmission against multi-antenna eavesdroppers.

This paper investigates a reconfigurable intelligent surface equipped unmanned aerial vehicle (RISeUAV)-enabled wireless powered communication network (WPCN) consisting of a ground hybrid access point (GHAP), a RISeUAV, and a ground wireless device (GWD). Specifically, the passive GWD first harvests energy through the downlink (DL) wireless power transfer (WPT) assisted by the RISeUAV, and then wireless information transfer (WIT) takes place in the RISeUAV-aided uplink (UL) communications. Due to the partial reciprocity of wireless channels, the unavailable DL channel state information (CSI) can be estimated by an outdated version of the UL CSI. Considering the negative effect of this outdated CSI and a non-linear energy-harvester (NLEH), we formulate a two-timescale active and passive beamforming (TTAPB) framework for maximizing the ergodic throughput (ET) and derive a closed-form expression of the ET, based on which the joint optimization of time allocation for WPT/WIT and UAV placement with two-timescale active and passive beamforming (JOTAUP-TTAPB) scheme is proposed. As for the solution of the JOTAUP-TTAPB scheme, although the objective function is non-convex with respect to the hovering altitude of the RISeUAV, it can be transformed into an equivalent exponential function of fractional forms that facilitates the utilization of a golden-section-search-aided Dinkelbach (GSS-D) algorithm to obtain a jointly optimal solution. Simulation results demonstrate the effectiveness of the TTAPB framework by validating our ET analysis. Furthermore, the JOTAUP-TTAPB scheme outperforms several benchmark schemes in spite of performance degradations of all schemes due to imperfect channel reciprocity, exhibiting remarkable robustness against outdated DL CSI. Moreover, the proposed JOTAUP-TTAPB scheme improves the performance bounds limited by NLEHs. Additionally, not only the GSS-D algorithm for the JOTAUP-TTAPB scheme shows a faster convergence than the golden-section-search-aided alternating optimization, but also the low-complexity joint optimization avoids optimality loss by exploiting semi-closed-form solutions.

In this paper, we investigate the amalgamation of cell-free (CF) and extremely large-scale multiple-input multiple-output (XL-MIMO) technologies, referred to as a CF XL-MIMO, as a promising advancement for enabling future mobile networks. To address the computational complexity and communication power consumption associated with conventional centralized optimization, we focus on user-centric dynamic networks in which each user is served by an adaptive subset of access points (AP) rather than all of them. We begin our research by analyzing a joint resource allocation problem for energy-efficient CF XL-MIMO systems, encompassing cooperative clustering and power control design, where all clusters are adaptively adjustable. Then, we propose an innovative double-layer multi-agent reinforcement learning (MARL)-based scheme, which offers an effective strategy to tackle the challenges of high-dimensional signal processing. In the section of numerical results, we compare various algorithms with different network architectures. These comparisons reveal that the proposed MARL-based cooperative architecture can effectively strike a balance between system performance and communication overhead, thereby improving energy efficiency performance. It is important to note that increasing the number of user equipments participating in information sharing can effectively enhance SE performance, which also leads to an increase in power consumption, resulting in a non-trivial trade-off between the number of participants and EE performance.

This paper proposes a grant-free massive access scheme based on the millimeter wave (mmWave) extra-large-scale multiple-input multiple-output (XL-MIMO) to support massive Internet-of-Things (IoT) devices with low latency, high data rate, and high localization accuracy in the upcoming sixth-generation (6G) networks. The XL-MIMO consists of multiple antenna subarrays that are widely spaced over the service area to ensure line-of-sight (LoS) transmissions. First, we establish the XL-MIMO-based massive access model considering the near-field spatial non-stationary (SNS) property. Then, by exploiting the block sparsity of subarrays and the SNS property, we propose a structured block orthogonal matching pursuit algorithm for efficient active user detection (AUD) and channel estimation (CE). Furthermore, different sensing matrices are applied in different pilot subcarriers for exploiting the diversity gains. Additionally, a multi-subarray collaborative localization algorithm is designed for localization. In particular, the angle of arrival (AoA) and time difference of arrival (TDoA) of the LoS links between active users and related subarrays are extracted from the estimated XL-MIMO channels, and then the coordinates of active users are acquired by jointly utilizing the AoAs and TDoAs. Simulation results show that the proposed algorithms outperform existing algorithms in terms of AUD and CE performance and can achieve centimeter-level localization accuracy.

Compared with traditional half-duplex wireless systems, the application of emerging full-duplex (FD) technology can potentially double the system capacity theoretically. However, conventional techniques for suppressing self-interference (SI) adopted in FD systems require exceedingly high power consumption and expensive hardware. In this paper, we consider employing an intelligent reflecting surface (IRS) in the proximity of an FD base station (BS) to mitigate SI for simultaneously receiving data from uplink users and transmitting information to downlink users. The objective considered is to maximize the system weighted sum-rate by jointly optimizing the IRS phase shifts, the BS transmit beamformers, and the transmit power of the uplink users. To visualize the role of the IRS in SI cancellation, we first study a simple scenario with one downlink user and one uplink user. To address the formulated non-convex problem, a low-complexity algorithm based on successive convex approximation is proposed. For the more general case considering multiple downlink and uplink users, an efficient alternating optimization algorithm based on element-wise optimization is proposed. Numerical results demonstrate that the FD system with the proposed schemes can achieve a larger gain over the half-duplex system, and the IRS is able to achieve a balance between suppressing SI and providing beamforming gain.

Despite its potential for reducing the detection probability at the warden, the effectiveness of covert communication in practical situations is often hindered by harsh wireless signal propagation environments. Fortunately, intelligent reflecting surface (IRS) can establish programmable wireless channels to tackle this issue. In this paper, we propose two IRS-assisted finite-blocklength covert communication schemes to maximize the effective covert throughput (ECT) with equal and unequal transmit prior probabilities, respectively. First, we analyze the warden’s detection performance with its optimal detection threshold derived, which is the worst situation for the covert transmission. We jointly optimize the transmit power, transmission blocklength, prior transmission probability and IRS’s phase shifts to maximize ECT in the common scenario and packet-generation scenario, respectively, which covers a wide range of practical applications. The designed optimal phase shifts not only maximize the signal-to-noise ratio at the receiver, but also introduce uncertainty to the warden for covertness provisioning. The closed-form expressions of solutions indicate that there exists a non-trivial trade-off between ECT and covertness, and adopting unequal transmit prior probabilities is proved to perform better than its counterpart of equal probabilities. Finally, numerical results demonstrate the superior performance achieved by the proposed covert communication schemes.

This paper investigates the unsourced random access (URA) problem with a massive multiple-input multiple-output receiver that serves wireless devices in the near-field of radiation. We employ an uncoupled transmission protocol without appending redundancies to the slot-wise encoded messages. To exploit the channel sparsity for block length reduction while facing the collapsed sparse structure in the angular domain of near-field channels, we propose a sparse channel sampling method that divides the angle-distance (polar) domain based on the maximum permissible coherence. Decoding starts with retrieving active codewords and channels from each slot. We address the issue by leveraging the structured channel sparsity in the spatial and polar domains and propose a novel turbo-based recovery algorithm. Furthermore, we investigate an off-grid compressed sensing method to refine discretely estimated channel parameters over the continuum that improves the detection performance. Afterward, without the assistance of redundancies, we recouple the separated messages according to the similarity of the users’ channel information and propose a modifiedK-medoids method to handle the constraints and collisions involved in channel clustering. Simulations reveal that via exploiting the channel sparsity, the proposed URA scheme achieves high spectral efficiency and surpasses existing multi-slot-based schemes. Moreover, with more measurements provided by the overcomplete channel sampling, the near-field-suited scheme outperforms its counterpart of the far-field.

Deep learning (DL) has been increasingly adopted for channel state information (CSI) feedback to harness the performance gains promised by massive multiple-input multiple-output (MIMO). Existing DL-based feedback schemes prioritize the accuracy of CSI reconstruction, which results in substantial memory and computational demands, especially when they are unacceptable for user equipment (UE) with limited resources. In this paper, we propose an asymmetric pooling-based network for more efficient CSI compression, named PoolCsiNet, to reduce the associated overhead of exploiting convolutional neural networks (CNN) for CSI compression at the UE. By leveraging the local information of clustered physical channel models, PoolCsiNet incorporates a low-complexity amplitude-pooling algorithm in its encoder at the UE without requiring any trainable parameters. A corresponding decoder structure is also developed to firstly acquire a coarse CSI and then a lightweight feature refiner is constructed to enhance the coarse CSI reconstruction. The parameter-free encoder and CNN-based refiner constitute a novel asymmetric CSI network architecture. Thanks to the parameter-free design of the encoder, memory demand at the UE is minimized, thereby eliminating the need for joint training and parameter updating. Furthermore, considering the sparsity of indoor wireless channels, a PoolCsiNet+, with a dilated-amplitude-pooling (DAP) module, is further proposed to elevate the CSI reconstruction accuracy of the PoolCsiNet. Thanks to a pooling design tailored for clustered channel models, lossy compression of pooling hardly sacrifices CSI features and can be exploited to eliminate information redundancy in CSI. Experiments demonstrate that both asymmetric PoolCsiNet and PoolCsiNet+ significantly improve the quality of CSI reconstruction up to 4 dB compared with existing DL-based methods, while maintaining a memory-free profile and achieving a sevenfold reduction in computational overhead at the UE.

Multi-access edge computing (MEC) has been recognized as a promising technology for enhancing the computation capability for next generation wireless networks. This paper studies physical layer security for an MEC network, where multiple users desire to securely offload part of their computation tasks to a base station (BS) simultaneously using non-orthogonal multiple access (NOMA) subject to the potential overhearing of a malicious eavesdropper. The secrecy outage probability (SOP) is adopted as a secrecy performance metric of the computation offloading against eavesdropping attacks. We aim to minimize the total energy consumption of the MEC system subject to an individual SOP constraint for each user. To this end, we jointly design each user’s local computing bits, the transmit power, the secrecy code rates, as well as the successive interference cancellation decoding order at the BS side. As the formulated problem is highly non-convex and challenging to solve, we propose an efficient algorithm based on penalty dual decomposition (PDD) and sequential convex approximation methods to obtain an efficient suboptimal solution. To reduce the computational complexity, we further propose a reverse recursion (RR) algorithm and derive semi-closed-form solutions to the design problem. Numerical results are presented to validate the convergence and the effectiveness of our proposed algorithms. We show that the minimal total energy consumption obtained via either the PDD or RR method approaches the optimal performance of exhaustive search as the task duration increases. It is also demonstrated that the RR algorithm can achieve a comparable performance to that of the PDD algorithm while enjoying a much lower computational complexity.

In this paper, we investigate the design of energy-efficient beamforming for an ISAC system, where the transmitted waveform is optimized for joint multi-user communication and target estimation simultaneously. We aim to maximize the system energy efficiency (EE), taking into account the constraints of a maximum transmit power budget, a minimum required signal-to-interference-plus-noise ratio (SINR) for communication, and a maximum tolerable Cramér-Rao bound (CRB) for target estimation. We first consider communication-centric EE maximization. To handle the non-convex fractional objective function, we propose an iterative quadratic-transform-Dinkelbach method, where Schur complement and semi-definite relaxation (SDR) techniques are leveraged to solve the subproblem in each iteration. For the scenarios where sensing is critical, we propose a novel performance metric for characterizing the sensing-centric EE and optimize the metric adopted in the scenario of sensing a point-like target and an extended target. To handle the nonconvexity, we employ the successive convex approximation (SCA) technique to develop an efficient algorithm for approximating the nonconvex problem as a sequence of convex ones. Furthermore, we adopt a Pareto optimization mechanism to articulate the tradeoff between the communication-centric EE and sensing-centric EE. We formulate the search of the Pareto boundary as a constrained optimization problem and propose a computationally efficient algorithm to handle it. Numerical results validate the effectiveness of our proposed algorithms compared with the baseline schemes and the obtained approximate Pareto boundary shows that there is a non-trivial tradeoff between communication-centric EE and sensing-centric EE, where the number of communication users and EE requirements have serious effects on the achievable tradeoff.

Reconfigurable intelligent surface (RIS) has been recently regarded as a disruptive candidate technology for enabling next generation wireless communication. It can establish favorable propagation environment to facilitate low-power and spectrally efficient data transmission, possessing attractive potential to support massive access. However, the required activity detection and channel estimation for RIS-assisted massive access is quite challenging due to the passive nature of the conventional reflecting elements. To this end, this paper considers massive access for RIS-assisted communication systems with semi-passive elements, which can operate in sensing mode for receiving signals. Then, by exploiting the sparsity of the RIS-BS channel in the virtual angular domain as well as the sporadic transmission of massive connectivity, we formulate the joint activity detection and channel estimation as a special bilinear recovery problem, which is a combination of sparse matrix factorization, compressed sensing (CS)-based generalized multiple measurement vector (GMMV) problem and matrix completion. Furthermore, we propose a novel hierarchical message passing-based algorithm to address the problem, in which approximate message passing (AMP)-based approximations are adopted to reduce the computational complexity. Simulation results demonstrate the effectiveness of the proposed algorithm and its superior performance compared with state-of-the-art baseline schemes.

This paper unifies integrated sensing and communication (ISAC) and simultaneous wireless information and power transfer (SWIPT), by investigating a new multi-functional multiple-input multiple-output (MIMO) system that integrates wireless sensing, communication, and powering. In this system, a multi-antenna hybrid access point (H-AP) transmits wireless signals to communicate with a multi-antenna information decoding (ID) receiver, wirelessly charges a multi-antenna energy harvesting (EH) receiver, and performs radar target sensing based on the echo signal concurrently. Under this setup, we aim to reveal the fundamental performance tradeoff limits among sensing, communication, and powering, in terms of the estimation Cramér-Rao bound (CRB), achievable communication rate, and harvested energy, respectively. In particular, we consider two different target models for radar sensing, namely the point and extended targets, for which we are interested in estimating the target angle and the complete target response matrix, respectively. For both models, we define the achievable CRB-rate-energy (C-R-E) region and characterize its Pareto boundary by maximizing the achievable rate at the ID receiver, subject to the estimation CRB requirement for target sensing, the minimum harvested energy requirement at the EH receiver, and the maximum transmit power constraint at the H-AP. We obtain partitionable optimal transmit covariance matrix solutions to the two formulated problems by applying advanced convex optimization techniques. The numerical results demonstrate the optimal C-R-E region boundary achieved by our proposed design, as compared to the benchmark schemes based on time division and eigenmode transmission (EMT).

This paper studies a multi-antenna networked integrated sensing and communications (ISAC) system, in which a set of multi-antenna base stations (BSs) employ the coordinated transmit beamforming to serve multiple single-antenna communication users (CUs) and concurrently perform joint target detection by exploiting the echo signals. To facilitate target sensing, the BSs transmit dedicated sensing signals combined with their information signals. We consider two types of CU receivers with and without the capability of canceling the interference from the dedicated sensing signals, respectively. We also investigate two scenarios with and without time synchronization among the BSs. For the scenario with synchronization, the BSs can exploit the target-reflected signals over both the direct links (BS-to-target-to-originated-BS links) and the cross-links (BS-to-target-to-other-BSs links) for joint detection, while in the unsynchronized scenario, the BSs can only utilize the target-reflected signals over the direct links. For each scenario under different types of CU receivers, we optimize the coordinated transmit beamforming at the BSs to maximize the minimum detection probability over a particular targeted area, while guaranteeing the required minimum signal-to-interference-plus-noise ratio (SINR) constraints at the CUs. These SINR-constrained detection probability maximization problems are recast as non-convex quadratically constrained quadratic programs (QCQPs), which are then optimally solved via the semi-definite relaxation (SDR) technique. Numerical results show that for each considered scenario, the proposed ISAC design achieves enhanced target detection probability compared with various benchmark schemes. In particular, enabling time synchronization and sensing signal cancellation at the BSs is always beneficial for further improving the joint detection and communication performance.

Channel estimation (CE) plays a key role in reconfigurable intelligent surface (RIS)-aided multiple-input multiple-output (MIMO) communication systems, while it poses a challenging task due to the passive nature of RIS and the cascaded channel structures. In this paper, a partially decoupled atomic norm minimization (PDANM) framework is proposed for CE of RIS-aided MIMO systems, which exploits the three-dimensional angular sparsity of the channel. In particular, PDANM partially decouples the differential angles at the RIS from other angles at the base station and user equipment, reducing the computational complexity compared with existing methods. A reweighted PDANM (RPDANM) algorithm is proposed to further improve CE accuracy, which iteratively refines CE through a specifically designed reweighting strategy. Building upon RPDANM, we propose an iterative approach named RPDANM with adaptive phase control (RPDANM-APC), which adaptively adjusts the RIS phases based on previously estimated channel parameters to facilitate CE, achieving superior CE accuracy while reducing training overhead. Numerical simulations demonstrate the superiority of our proposed approaches in terms of running time, CE accuracy, and training overhead. In particular, the RPDANM-APC approach can achieve higher CE accuracy than existing methods within less than 30 percent training overhead while reducing the running time by tens of times.

As a revolutionary paradigm for green ultra-reliable low-latency communication (URLLC), reconfigurable intelligent surfaces (RISs) have been considered as a prominent architecture for enabling next-generation communication systems. Recently, a novel RIS framework, called simultaneous transmitting and reflecting (STAR-RIS), has been proposed to facilitate both transmission and reflection through the meta-material surface, leading to full-space coverage and even better beamforming flexibility than conventional RIS. This paper investigates an energy-efficient resource allocation design scheme for a STAR-RIS-aided downlink system under various STAR-RIS modes to deliver energy-efficient URLLC services by jointly optimizing the beamforming at the base station (BS) and STAR-RIS, subject to the given requirements on the rate, packet-error probability, and latency. Owing to the non-convex and NP-hard nature of the formulated problem, we propose an alternating optimization framework that obtains suboptimal solutions to the problems of beamforming design at the BS and STAR-RIS, respectively, in an iterative manner by exploiting fractional programming and successive convex approximation approaches. Simulation results confirm that the TS, ES, and MS modes of STAR-RIS achieve approximately$30\%-50\%$,$20\%-40\%$, and$10\%-15\%$, respectively better performance than a conventional reflecting-only RIS while guaranteeing strict reliability and latency requirements of URLLC. Specifically, among all the possible modes of STAR-RIS, the time-splitting mode renders an effective solution due to its better interference management.

Unmanned aerial vehicle (UAV) communication is of crucial importance in realizing heterogeneous practical wireless application scenarios. However, it is susceptible to the severe spectrum scarcity and interference issues since it operates in the unlicensed frequency band. To tackle those issues, a dynamic spectrum sharing UAV network adopting an anti-jamming technique is considered. Two intelligent spectrum allocation and trajectory optimization schemes are designed, capitalizing on the proposed external interaction and internal inference based frameworks. For the first scheme, a novel external interaction based hybrid online-offline multi-agent actor-critic and deep deterministic policy gradient (MA2C-DDPG) framework is proposed taking into account the hybrid characteristics of discrete spectrum allocation and continuous UAV trajectory. As for the second scheme, another novel framework, the deep active inference (DAI) based on internal inference is proposed, which minimizes the internal variational free energy. Moreover, a belief learning based method is exploited to enhance the agents’ perception and improve the action selection in the dynamic spectrum sharing environment. Extensive simulation results demonstrate the high efficiency of our proposed schemes. It is shown that our proposed schemes significantly improve the secondary network sum transmission rate compared to various benchmark schemes. Moreover, the proposed MA2C-DDPG and DAI frameworks demonstrate the advantages in improving the training stability and convergence speed.

This paper considers intelligent reflecting surface (IRS)-aided simultaneous wireless information and power transfer (SWIPT) in a multi-user multiple-input single-output (MISO) interference channel (IFC), where multiple transmitters (Txs) serve their corresponding receivers (Rxs) in a shared spectrum with the aid of IRSs. Our goal is to maximize the sum rate of the Rxs by jointly optimizing the transmit covariance matrices at the Txs, the phase shifts at the IRSs, and the resource allocation subject to the individual energy harvesting (EH) constraints at the Rxs. Towards this goal and based on the well-known power splitting (PS) and time switching (TS) receiver structures, we consider three practical transmission schemes, namely the IRS-aided hybrid TS-PS scheme, the IRS-aided time-division multiple access (TDMA) scheme, and the IRS-aided TDMA-D scheme. The latter two schemes differ in whether the Txs employ deterministic energy signals known to all the Rxs. Despite the non-convexity of the three optimization problems corresponding to the three transmission schemes, we develop computationally efficient algorithms to address them suboptimally, respectively, by capitalizing on the techniques of alternating optimization (AO) and successive convex approximation (SCA). Moreover, we conceive feasibility checking methods for these problems, based on which the initial points for the proposed algorithms are constructed. Simulation results demonstrate that our proposed IRS-aided schemes significantly outperform their counterparts without IRSs in terms of sum rate and maximum EH requirements that can be satisfied under various setups. In addition, the IRS-aided hybrid TS-PS scheme generally achieves the best sum rate performance among the three proposed IRS-aided schemes, and if not, increasing the number of IRS elements can always accomplish it.

Reconfigurable intelligent surface (RIS) is a disruptive technology to enhance the performance of physical-layer key generation (PKG) thanks to its ability to smartly customize the radio environments. Existing RIS-assisted PKG methods are mainly based on the idealistic assumption of an independent and identically distributed (i.i.d.) channel model at both the base station (BS) and the RIS. However, the i.i.d. model is inaccurate for a typical RIS in an isotropic scattering environment and neglecting the existence of channel spatial correlation would possibly degrade the PKG performance. In this paper, we establish a general spatially correlated channel model and propose a new channel probing framework based on the transmit and the reflective beamforming. We derive a closed-form key generation rate (KGR) expression and formulate an optimization problem, which is solved by using the low-complexity Block Successive Upper-bound Minimization (BSUM) with Mirror-Prox method. Simulation results show that compared to the existing methods based on the i.i.d. fading model, our proposed method achieves about 5 dB transmit power gain when the spacing between two neighboring RIS elements is a quarter of the wavelength. Also, the KGR increases significantly with the number of RIS elements while that increases marginally with the number of BS and user antennas.

Cell-free massive multiple-input multiple-output (mMIMO) is a promising technology to empower next-generation mobile communication networks. In this paper, to address the computational complexity associated with conventional fingerprint positioning, we consider a novel cooperative positioning architecture that involves certain relevant access points (APs) to establish positioning similarity coefficients. Then, we propose an innovative joint positioning and correction framework employing multi-agent reinforcement learning (MARL) to tackle the challenges of high-dimensional sophisticated signal processing, which mainly leverages on the received signal strength information for preliminary positioning, supplemented by the angle of arrival information to refine the initial position estimation. Moreover, to mitigate the bias effects originating from remote APs, we design a cooperative weighted K-nearest neighbor (Co-WKNN)-based estimation scheme to select APs with a high correlation to participate in user positioning. In the numerical results, we present comparisons of various user positioning schemes, which reveal that the proposed MARL-based positioning scheme with Co-WKNN can effectively improve positioning performance. It is important to note that the cooperative positioning architecture is a critical element in striking a balance between positioning performance and computational complexity.

This paper investigates the performance tradeoff for a multi-antenna integrated sensing and communication (ISAC) system with simultaneous information multicasting and multi-target sensing, in which a multi-antenna base station (BS) sends the common information messages to a set of single-antenna communication users (CUs) and estimates the parameters of multiple sensing targets based on the echo signals concurrently. We consider two target sensing scenarios without and with prior target knowledge at the BS, in which the BS is interested in estimating the complete multi-target response matrix and the target reflection coefficients/angles, respectively. First, we consider the capacity-achieving transmission and characterize the fundamental tradeoff between the achievable rate and the multi-target estimation Cramér-Rao bound (CRB) accordingly. To this end, we design the optimal transmit signal covariance matrix at the BS to minimize the estimation CRB for each of the two scenarios, subject to the minimum multicast rate requirement and the maximum transmit power constraint. It is shown that the optimal covariance matrix consists of two parts for ISAC and dedicated sensing, respectively. Next, we consider the transmit beamforming designs, in which the BS sends one information beam together with multiple a-priori known dedicated sensing beams for effective ISAC and each CU can cancel the interference caused by the sensing signals. By exploiting the successive convex approximation (SCA) technique, we develop efficient algorithms to obtain the joint information and sensing beamforming solutions to the resultant rate-constrained CRB minimization problems. Finally, we provide numerical results to validate the CRB-rate (C-R) tradeoff achieved by our proposed designs, as compared to two benchmark schemes, namely the isotropic transmission and the joint beamforming without sensing interference cancellation. It is shown that the proposed optimal transmit covariance solution achieves much better C-R performance than the benchmark schemes and the proposed joint beamforming with sensing interference cancellation performs close to the optimal transmit covariance solution when the number of CUs is small. We also conduct simulations to show the practical estimation performance achieved by our proposed designs, by considering randomly generated information signals and practical estimators.

The utilization of a large-scale antenna array has led to substantial performance improvements in anti-jamming communications. However, due to the practical constraints of hardware cost and power consumption, deploying such a large-scale antenna array at the user side is impractical. Inspired by the remarkable advantages of reconfigurable intelligent surfaces (RIS), we propose an active-passive cascaded RIS-aided receiver architecture that facilitates the cost- and energy-efficient deployment of a large-scale antenna array at the user side, while also providing additional degrees-of-freedom for effective beamforming design. Building upon this architectural framework and taking into account the practical imperfections in the angular channel state information (CSI), we formulate a worst-case achievable rate maximization problem for anti-jamming communications. To address the challenges posed by the intractable non-convex design problem, we present a low-complexity optimization framework that obtains semi-closed-form solutions. Specifically, we first develop a Pareto-dual scheme to handle the general power constraints in devising the optimal precoder for the base station. Subsequently, by introducing a novel anti-jamming criterion and employing the discretization method to transform the imperfect CSI of jammers into a robust form, we derive two jamming-nulling feasibility conditions and a unified unit-modulus zero-forcing scheme to determine the coefficients of the passive RIS. To strike a satisfactory balance between complexity and performance, we further design three computationally-efficient algorithms based on alternating majorization-minimization (AMM) and conventional/modified cyclic coordinate descent (C/M-CCD) methods to obtain the coefficients of the active RIS. Finally, through comprehensive numerical simulations, we validate the effectiveness of the proposed architecture and optimization framework, demonstrating their capacity to achieve exceptional performance in a cost-effective manner.

In this paper, we study low-complexity linear precoding for downlink massive multiple-input multiple-output (MIMO) systems, exploiting a statistical method. In sharp contrast to traditional linear precoding algorithms, our proposed efficient randomized iterative precoding algorithm (ERIPA) not only avoids costly matrix inversion but also considers the complexity reduction of matrix multiplication involved, thus enabling more efficient linear precoding. Additionally, ERIPA is demonstrated to have both exponentially fast and global convergence, making it adaptable to various practical scenarios of massive MIMO. We also investigate the convergence phenomenon of ERIPA in relation to the selection of the sampling distribution during random iterations. After that, the concept of conditional sampling is introduced to ERIPA such that significant system potential can be beneficially exploited in terms of both precoding performance and computational complexity. Finally, simulation results regarding the downlink massive MIMO are presented to confirm the superiorities of the proposed ERIPA.

The aim of this paper is to enhance the performance of an integrated sensing and communications (ISAC) system in the multiuser multiple-input multiple-output (MIMO) downlink in which a two-dimensional (2D) fluid antenna system (FAS) with multiple activated ports is employed at the base station (BS) to maximize the sum-rate of the downlink users subject to a sensing constraint. The unique feature of this setup is that the locations of the antenna ports at the FAS can be optimized jointly with the precoding design to achieve a higher sum-rate. The required optimization problem is however NP-hard. To overcome this, we start by considering the perfect channel state information (CSI) scenario where all the port CSI is available. Deep reinforcement learning is utilized to build an end-to-end learning framework for the joint optimization problem. In particular, by fixing the activated ports, we adopt a primal-dual based learning algorithm to design a constraint-aware neural network for optimizing the ISAC precoder. Then, by using the neural precoding network to calculate the reward, we adopt the deep reinforcement learning algorithm to design the port selection and precoder jointly. An advantage actor and critic (A2C) algorithm is proposed to train the policy, in which the actor network uses the pointer network to learn the stochastic policy and the critic network adopts the Long Short-Term Memory (LSTM) encoder architecture to learn the expected reward from the observations. Afterwards, the partial CSI case is addressed, where we propose a masked autoencoder (MAE) induced channel extrapolation for predicting all the CSI to facilitate the joint design. Simulation results demonstrate the promising performance of using FAS for multiuser MIMO and also validate the proposed learning-based scheme.

This paper proposes an integrated sensing, navigation, and communication (ISNC) framework for safeguarding unmanned aerial vehicle (UAV)-enabled wireless networks against a mobile eavesdropping UAV (E-UAV). To cope with the mobility of the E-UAV, the proposed framework advocates the dual use of artificial noise transmitted by the information UAV (I-UAV) for simultaneous jamming and sensing to facilitate navigation and secure communication. In particular, the I-UAV communicates with legitimate downlink ground users, while avoiding potential information leakage by emitting jamming signals, and estimates the state of the E-UAV with an extended Kalman filter based on the backscattered jamming signals. Exploiting the estimated state of the E-UAV in the previous time slot, the I-UAV determines its flight planning strategy, predicts the wiretap channel, and designs its communication resource allocation policy for the next time slot. To circumvent the severe coupling between these three tasks, a divide-and-conquer approach is adopted. The online navigation design has the objective to minimize the distance between the I-UAV and a pre-defined destination point considering kinematic and geometric constraints. Subsequently, given the predicted wiretap channel, the robust resource allocation design is formulated as an optimization problem to achieve the optimal trade-off between sensing and communication in the next time slot, while taking into account the wiretap channel prediction error and the quality-of-service (QoS) requirements of secure communication. To account for the E-UAV state sensing uncertainty and the resulting wiretap channel prediction error, we employ a fully-connected neural network to model the complicated mapping between the state estimation error variance and an upper bound on the channel prediction error, which facilitates the development of a low-complexity suboptimal user scheduling and precoder design algorithm. Simulation results demonstrate the superior performance of the proposed design compared with baseline schemes and validate the benefits of integrating sensing and navigation into secure UAV communication systems. We reveal that the dual use of artificial noise can improve both sensing and jamming and that navigation is more important for improving the trade-off between sensing and communications than communication resource allocation.

Spectrum sharing is promising as a solution to address the spectrum crunch by enabling the coexistence of different networks in the same frequency band. However, interference from concurrent transmissions remains an obstacle to further enhance spectral efficiency. Therefore, to overcome the bottleneck caused by multi-user interference, both rate-splitting multiple access (RSMA)-enabled underlay and overlay spectrum-sharing strategies are proposed in this paper. To facilitate a robust resource allocation design, the common and the private beamforming vectors as well as the common rate allocation are jointly optimized under the norm-bounded channel state information (CSI) error model to maximize the worst-case weighted sum rate (WSR) of the secondary networks. To address the formulated challenging non-convex quadratically-constrained resource allocation optimization problems, a computationally efficient successive convex approximation (SCA)-based algorithm capitalizing on semidefinite relaxation (SDR) is proposed. Simulation results demonstrate that the proposed algorithms outperform non-orthogonal multiple access (NOMA)-based benchmark schemes in worst-case WSR and robustness. Moreover, the results indicate that the proposed novel RSMA-enabled overlay spectrum-sharing strategy can offer a higher flexibility in resource allocation compared to their underlay counterparts. Furthermore, the tradeoff between interference management and spectral performance enhancement for the proposed RSMA-enabled overlay spectrum-sharing strategy is unveiled.

Vehicle-to-infrastructure (V2I) communications have been regarded as an emerging application in next-generation wireless networks. However, guaranteeing high-quality wireless communications in high-mobility scenarios remains a major challenge. In this paper, we investigate the deployment of reconfigurable intelligent surface (RIS) for improving the communication performance of V2I systems. In particular, integrated sensing and communication (ISAC) signals are exploited to facilitate sensing-assisted beamforming. Aiming at maximizing the achievable rate, two deep learning-based predictive beamforming mechanisms are proposed. First, a two-stage beamforming design is devised, where the channel state information (CSI) is estimated based on the echo signals and predicted by a dedicated neural network for time-varying channels. Then, the transmit beamforming vector at the base station (BS) and the reflect beamforming matrix at the RIS are jointly optimized. To further reduce the computational complexities, we develop an end-to-end beamforming design by employing the parameter sharing mechanism and weighted loss function. Simulation results demonstrate that the proposed algorithms can achieve an outstanding data rate that approaches the upper bound exploiting perfect CSI. In particular, the end-to-end design exhibits remarkable robustness against the impact of noise and achieves outstanding sensing-assisted beamforming performance, especially at the low signal-to-noise ratio region.

Benefitting from the vast spatial degrees of freedom, the amalgamation of integrated sensing and communication (ISAC) and massive multiple-input multiple-output (MIMO) is expected to simultaneously improve spectral and energy efficiencies as well as the sensing capability. However, a large number of antennas deployed in massive MIMO-ISAC raises critical challenges in acquiring both accurate channel state information and target parameter information. To overcome these two challenges with a unified framework, we first analyze their underlying system models and then propose a novel tensor-based approach that addresses both the channel estimation and target sensing problems. Specifically, by parameterizing the high-dimensional communication channel exploiting a small number of physical parameters, we associate the channel state information with the sensing parameters of targets in terms of angular, delay, and Doppler dimensions. Then, we propose a shared training pattern adopting the same time-frequency resources such that both the channel estimation and target parameter estimation can be formulated as a canonical polyadic decomposition problem with a similar mathematical expression. On this basis, we first investigate the uniqueness condition of the tensor factorization and the maximum number of resolvable targets by utilizing the specific Vandermonde structure. Then, we develop a unified tensor-based algorithm to estimate the parameters including angles, time delays, Doppler shifts, and reflection/path coefficients of the targets/channels. In addition, we propose a segment-based shared training pattern to facilitate the channel and target parameter estimation for the case with significant beam squint effects. Simulation results verify our theoretical analysis and the superiority of the proposed unified algorithms in terms of estimation accuracy, sensing resolution, and training overhead reduction.

Integrated sensing and communication (ISAC) technology effectively enables spectrum and hardware sharing between radar and communication. Moreover, ISAC outperforms traditional separate radar and communication systems in terms of both power consumption and spectral efficiency. This paper investigates the dual-functional (DF) constant modulus waveform design for simultaneously transmitting and reconfigurable intelligent surface (STAR-RIS)-aided ISAC. To investigate the performance trade-off, the weighted sum of multi-user interference (MUI) energy and waveform discrepancies is minimized via jointly optimizing the transmit waveform and the reflection and transmission coefficient matrices at STAR-RIS. Furthermore, both cases of independent and coupled phase shifts at STAR-RIS are investigated. For independent phase shifts, we develop an alternating direction method of multipliers (ADMM)-based algorithm to decouple the original problem into several tractable subproblems that facilitates the derivation of a closed-form solution to each subproblem. In the scenario with the coupled phase shifts, we first formulate the optimization problem as a Markov decision process, employing a twin delayed deep deterministic policy gradient (TD3)-based deep reinforcement learning approach to address it. Simulation results verify the effectiveness of the proposed schemes, demonstrating STAR-RIS’s superiority over conventional RIS. Moreover, the adopted protocol of STAR-RIS can maintain an excellent balance between performance and complexity.

This paper investigates simultaneous wireless information and power transfer (SWIPT)-enabled cell-free massive multiple-input multiple-output (CF-mMIMO) networks with power splitting (PS) receivers and non-orthogonal multiple access (NOMA). By exploiting the conjugated beamforming method, the closed-form expressions of the information rate and the total harvested power at each user equipment (UE) are derived. To improve the system spectral efficiency, a sum rate maximization problem is formulated subjecting to the quality of service requirement at each UE and the power budget constraint at each access point by optimizing the UE clustering, the power control coefficients, and the PS ratios. To solve the formulated non-convex and mixed combinatorial problem, a machine learning-based approach is designed. Particularly, the UE clustering is first optimized by using a K-means based method and then the power control coefficients and the PS ratios are jointly optimized by a proposed multi-agent deep Q-network (MA-DQN) based method. The impact of the discount factor of the MA-DQN based method on the derived result is discussed. It is proved that by setting the discount factor as zero, the performance loss is negligible. Based on this observation, a zero-discount MA-DQN (0-γ MA-DQN) based method is further proposed to improve the computational efficiency. Also, the computational complexity of the proposed machine learning-based approach is analyzed. Simulation results show that the proposed machine learning-based approach outperforms various existing approaches. Moreover, it indicates that CF-mMIMO and NOMA could enhance the propagation performance of SWIPT while the proposed machine learning-based approach could facilitate resource allocation.

This paper studies a novel movable antenna (MA)-enhanced multiple-input multiple-output (MIMO) system to leverage the corresponding spatial degrees of freedom (DoFs) for improving the performance of wireless communications. We aim to maximize the achievable rate by jointly optimizing the MA positions and the transmit covariance matrix based on statistical channel state information (CSI). To solve the resulting design problem, we develop a constrained stochastic successive convex approximation (CSSCA) algorithm applicable for the general movement mode. Furthermore, we propose two simplified antenna movement modes, namely the linear movement mode and the planar movement mode, to facilitate efficient antenna movement and reduce the computational complexity of the CSSCA algorithm. Numerical results show that the considered MA-enhanced system can significantly improve the achievable rate compared to conventional MIMO systems employing uniform planar arrays (UPAs) and that the proposed planar movement mode performs closely to the performance upper bound achieved by the general movement mode.

Channel estimation (CE) plays a key role in recon-figurable intelligent surface (RIS)-aided multiple-input multiple-output (MIMO) systems, while it is challenging due to the passive nature of RIS and the sophisticated cascaded channel structures. In this paper, a partially decoupled atomic norm minimization (PDANM) approach is proposed for the CE in RIS-aided MIMO systems. In particular, PDANM inherits the benefit of atomic norm minimization (ANM) for exploiting the three-dimensional angular structure of the channel in a grid-less manner that achieves a high CE accuracy. Besides, PDANM can partially decouple the differential angles at the RIS from other angular parameters at the base station and user equipment, reducing the computational complexity compared with other ANM-based methods. Numerical simulations illustrate that our proposed approach can significantly reduce the required computational complexity with a slight CE accuracy loss.

Reconfigurable intelligent surface (RIS) has the potential to significantly enhance the physical layer security by reconfiguring the wireless propagation environment. However, due to the hostile nature of potential eavesdroppers and the cascaded channel brought by the RIS, acquiring perfect channel state information (CSI) of the eavesdroppers is challenging. Worse still, if the eavesdroppers are equipped with multiple antennas, the design of the optimal phase-shift, power allocation, and secure transmission data rate are intractable due to the couplings of random channel matrices in the outage probability. To overcome these challenges, this paper for the first time reveals an analytical transformation for handling the outage probabilistic constraint in the secure energy efficiency maximization problem due to multi-antenna eavesdropper. The resultant problem is readily handled under the alternating maximization framework. Simulation results unveil that the proposed probabilistic constraint transformation and the associated optimization algorithm provide superior secure energy efficiency over the baseline schemes of random phase-shift, fixed phase-shift, RIS ignoring CSI uncertainty, and secure transmission without RIS.

Computation offloading has become a popular solution to support computationally intensive and latency-sensitive applications by transferring computing tasks to mobile edge servers (MESs) for execution, which is known as mobile/multi-access edge computing (MEC). To improve the MEC performance, it is required to design an optimal offloading strategy that includes offloading decision (i.e., whether offloading or not) and computational resource allocation of MEC. The design can be formulated as a mixed-integer nonlinear programming (MINLP) problem, which is generally NP-hard and its effective solution can be obtained by performing online inference through a well-trained deep neural network (DNN) model. However, when the system environments change dynamically, the DNN model may lose efficacy due to the drift of input parameters, thereby decreasing the generalization ability of the DNN model. To address this unique challenge, in this paper, we propose a multi-head ensemble multi-task learning (MEMTL) approach with a shared backbone and multiple prediction heads (PHs). Specifically, the shared backbone will be invariant during the PHs training and the inferred results will be ensembled, thereby significantly reducing the required training overhead and improving the inference performance. As a result, the joint optimization problem for offloading decision and resource allocation can be efficiently solved even in a time-varying wireless environment. Experimental results show that the proposed MEMTL outperforms benchmark methods in both the inference accuracy and mean square error without requiring additional training data.

This paper investigates the problem of spectral efficiency maximization in an underlay cognitive radio network (CRN) utilizing rate-splitting multiple access (RSMA) transmission for MISO downlink under short packet communications and imperfect channel estimation information. In particular, we focus on an effective transmit beamforming design at the cognitive base station while satisfying the requirements of ultra-reliable and low-latency communication (URLLC), interference temperature, power budget, and minimum throughput. We model the dynamic resource allocation problem as a Markov decision process (MDP) and employ deep reinforcement learning techniques, specifically the deep deterministic policy gradient (DDPG) and proximal policy optimization (PPO) algorithms, while taking into account the time-varying channel conditions. Simulation results demonstrate that the DDPG algorithm outperforms PPO at low interference temperatures for the primary receiver, while the opposite holds at high interference temperatures. Moreover, the considered RSMA system for CRN outperforms traditional multi-user linear precoding and power-domain multiple access schemes while maintaining small packet sizes and high reliability.

Automatic modulation recognition (AMR) plays a vital role in non-cooperative communication systems, which is an important technological component of blind signal processing. The application of deep learning (DL) methods in the field of modulation recognition has shown tremendous potential, greatly surpassing the performance of traditional methods. Existing DL-based AMR schemes employ modules of convolution and attentions to capture the local patterns and long-range dependencies for improving the performance of modulation classification, e.g., transformer-based methods. However, the complicated structure of existing artificial neural networks incur exceedingly long running time that hinders the practice implementation of these methods. Against this background, for the first time, this paper shows that the commonly adopted convolution and attentions are not necessary modules for the signal recognition. Specifically, we propose a lightweight architecture exploiting the multilayer perceptrons (MLP) for signal modulation classification, named SigMixer, which includes layers of token-mixing MLP and channel-mixing MLP that are interleaved for enabling spatial and temporal interaction. The proposed SigMixer not only has a simpler structure and lower computational complexity than the existing state-of-the-art approaches, but can also achieve a competitive performance with the formers. Experimental results show that compared with state-of-the-art approaches, SigMixer achieves better recognition accuracy, with 1.44%-10.65% improvement. Besides, the recognition accuracy can reach 89.36% for a low signal-to-noise ratio (SNR) = - 2 dB.

Covert communication assisted by intelligent reflecting surface (IRS) has been widely investigated. Specifically, IRS can reconfigure wireless propagation environment to introduce uncertainty to the warden for covertness provisioning. In this paper, we propose an IRS-assisted finite-blocklength covert communication scheme with unequal transmit prior probabilities (UTPP) resulting from random packet generation at the transmitter. First, we analyze the warden's detection performance with its optimal detection threshold derived, which is the worst case for covert transmission. Then, we jointly optimize the transmit power, the blocklength, the phase shifts of IRS, and the transmit prior probabilities to maximize the effective covert throughput (ECT). Theoretical analysis reveal that UTPP can perform better tradeoff between ECT and covertness than equal transmit prior probabilities. Finally, numerical results demonstrate the superiority of the proposed covert communication scheme with UTPP.

Movable antennas (MAs) are a promising paradigm to enhance the spatial degrees of freedom of conventional multi-antenna systems by flexibly adapting the positions of the antenna elements within a given transmit area. In this paper, we model the motion of the MA elements as discrete movements and study the corresponding resource allocation problem for MA-enabled multiuser multiple-input single-output (MISO) communication systems. Specifically, we jointly optimize the beamforming and the MA positions at the base station (BS) for the minimization of the total transmit power while guaranteeing the minimum required signal-to-interference-plus-noise ratio (SINR) of each individual user. To obtain the globally optimal solution to the formulated resource allocation problem, we develop an iterative algorithm capitalizing on the generalized Bender's decomposition with guaranteed convergence. Our numerical results demonstrate that the proposed MA-enabled communication system can significantly reduce the BS transmit power and the number of antenna elements needed to achieve a desired performance compared to state-of-the-art techniques, such as antenna selection. Furthermore, we observe that refining the step size of the MA motion driver improves performance at the expense of a higher computational complexity.

Resource allocation is of crucial importance in wireless communications. However, it is extremely challenging to design efficient resource allocation schemes for future wireless communication networks since the formulated resource allocation problems are generally non-convex and consist of various coupled variables. Moreover, the dynamic changes of practical wireless communication environment and user service requirements thirst for efficient real-time resource allocation. To tackle these issues, a novel partially observable deep multi-agent active inference (PODMAI) framework is proposed for realizing intelligent resource allocation. A belief based learning method is exploited for updating the policy by minimizing the variational free energy. A decentralized training with a decentralized execution multi-agent strategy is designed to overcome the limitations of the partially observable state information. Exploited the proposed framework, an intelligent spectrum allocation and trajectory optimization scheme is developed for a spectrum sharing unmanned aerial vehicle (UAV) network with dynamic transmission rate requirements as an example. Simulation results demonstrate that our proposed framework can significantly improve the sum transmission rate of the secondary network compared to various benchmark schemes. Moreover, the convergence speed of the proposed PODMAI is significantly improved compared with the conventional reinforcement learning framework. Overall, our proposed framework can enrich the intelligent resource allocation frameworks and pave the way for realizing real-time resource allocation.

In this paper, we investigate the beamforming design for an integrated sensing and communication (ISAC) system involved full-duplex (FD) communications. Specifically, an FD ISAC base station (BS) performs target detection and communicates with multiple downlink users and uplink users reusing the same time and frequency resources. We jointly optimize the downlink dual-functional transmit signal and the uplink receive beamformers at the BS and the transmit power at the uplink users. The problem is formulated to minimize the total transmit power of the system while ensuring the communication and sensing requirements. The downlink and uplink transmissions are tightly coupled, making the joint optimization challenging. To solve this intractable problem, we first determine the receive beamformers in closed forms with respect to the BS transmit beamforming and the user transmit power and then suggest an iterative solution to the remaining problem. We demonstrate via numerical results that the optimized FD communication-based ISAC leads to power efficiency improvement compared to conventional ISAC with HD communication.

To guarantee excellent reliability performance in ultra-reliable low-latency communications (URLLC), pragmatic precoder design is an effective approach. However, an efficient precoder design highly depends on the accurate instantaneous channel state information at the transmitter (ICSIT), which however, is not always available in practice. To overcome this problem, in this paper, we focus on the orthogonal time frequency space (OTFS)-based URLLC system and adopt a deep learning (DL) approach to directly predict the precoder for the next time frame to minimize the frame error rate (FER) via implicitly exploiting the features from estimated historical channels in the delay-Doppler domain. By doing this, we can guarantee the system reliability even without the knowledge of ICSIT. To this end, a general precoder design problem is formulated where a closed-form theoretical FER expression is specifically derived to characterize the system reliability. Then, a delay-Doppler domain channels-aware convolutional long short-term memory (CLSTM) network (DDCL-Net) is proposed for predictive precoder design. In particular, both the convolutional neural network and LSTM modules are adopted in the proposed neural network to exploit the spatial-temporal features of wireless channels for improving the learning performance. Finally, simulation results demonstrated that the FER performance of the proposed method approaches that of the perfect ICSI-aided scheme.

This paper studies the robust resource allocation design for secure intelligent reflecting surface (IRS)-aided wireless-powered communication networks (WPCN). Specifically, deploying an IRS can establish favorable end-to-end radio propagation environment for achieving the desired performance gain in secure wireless-powered systems. We aim to minimize the total hybrid base station (HBS) transmit power by jointly designing the active transmitting and receiving beamforming at the HBS, and the passive beamforming at the IRS taking into account the secrecy rate outage requirement and the harvested power constraints of the legitimate devices. To handle the optimization problem, we propose an efficient iterative suboptimal algorithm, which attains a Karush-Kuhn-Tucker (KKT) solution of the transformed problem. Simulation results unveil that the proposed scheme can dramatically reduce the HBS transmit power over various baseline schemes. Also, our results show the superiority of IRS-aided secure communication in wireless-powered systems.

For integrated sensing and communication (ISAC) systems, the desired channel variables by communication and sensing tasks vary with different timescales. For sensing, one is mainly interested in the state information (e.g., delays, angles, Doppler frequencies, etc.) of individual multi-path channel components, which evolves much more slowly than the composite channel state information (CSI) required by communications. In this paper, by exploiting the double timescales for sensing and communication, a novel technique termed as delay-Doppler alignment modulation (DDAM) is investigated, which is an appealing technique for ISAC systems, since the sensing result of resolvable multi-paths can be directly exploited for delay-Doppler compensation and path-based beamforming of DDAM. We first show that with perfect CSI, as long as the number of base station (BS) antennas is no smaller than that of resolvable multi-paths, the proposed DDAM is able to transform the time-frequency double selective-fading channel into a simple additive white Gaussian noise (AWGN) channel for inter-symbol interference (ISI)-free communication without requiring the conventional channel equalization or multi-carrier transmission. We then present the DDAM-based signal processing for ISAC, and the resulting communication performance with imperfectly sensed CSI is studied. Simulation results demonstrate that the proposed DDAM-based ISAC can achieve higher communication rate compared to orthogonal frequency division multiplexing (OFDM) and DFT-spread(s)-OFDM, while guaranteeing high sensing performance.

Extremely large-scale multiple-input-multiple-output (XL-MIMO) is a promising technology for the future sixth-generation (6G) networks to achieve higher performance. In practice, various linear precoding schemes, such as zero-forcing (ZF) and regularized zero-forcing (RZF) precoding, are capable of achieving both large spectral efficiency (SE) and low bit error rate (BER) in traditional massive MIMO (mMIMO) systems. However, these methods are not efficient in extremely large-scale regimes due to the inherent spatial non-stationarity and high computational complexity. To address this problem, we investigate a low-complexity precoding algorithm, e.g., randomized Kaczmarz (rKA), taking into account the spatial non-stationary properties in XL-MIMO systems. Furthermore, we propose a novel mode of randomization, i.e., sampling without replacement rKA (SwoR-rKA), which enjoys a faster convergence speed than the rKA algorithm. Besides, the closed-form expression of SE considering the interference between subarrays in downlink XL-MIMO systems is derived. Numerical results show that the complexity given by both rKA and SwoR-rKA algorithms has 51.3% reduction than the traditional RZF algorithm with similar SE performance. More importantly, our algorithms can effectively reduce the BER when the transmitter has imperfect channel estimation.

Downlink transmission of non-orthogonal multiple access visible light communication systems empowered by an unmanned aerial vehicle (UAV) is considered for multiplexing enhanced mobile broadband (eMBB) and massive machine type communication (mMTC) services. Accordingly, a resource allocation problem of joint transmit power control and motion trajectory design of the DAVs is formulated, whose goal is to characterize a multi-objective trade-off as a weighted sum of the UAVs' power consumption and the perceived quality-of-experience (QoE) of eMBB users, while ensuring the eMBB and mMTC service-specific requirements. We leverage an alternative decomposition and tools from convex optimization and actorcritic multi-agent deep reinforcement learning to address this problem in an iterative fashion. We analytically derive the upper-and lower-bounds on the reward of the DAVs as the learning agents and demonstrate that the proposed resource allocation method outperforms the similar scheme in literature, by up to 17% average reduced power consumption, as well as 12% average perceived QoE gain.

In this work, we consider the optimality of signalling for covert sensing, where a legitimate receiver intends to estimate unknown variables based on the received signals from a transmitter, while ensuring that the probability of these signals being detected by a warden Willie is negligible. Specifically, we consider additive white Gaussian noise (AWGN) for both the estimation channel and detection channel, based on which we first reveal that Gaussian signalling is not optimal in terms of maximizing the estimation accuracy (e.g., maximizing the Fisher information) subject to a covertness constraint, e.g., guaranteeing a Kullback-Leibler divergence being no large than a specific value. To this end, we explicitly show that a skew normal distribution with an optimized skew parameter can achieve a higher estimation accuracy than a normal distribution subject to the same covertness constraint. Furthermore, we develop a framework based on calculus of variations and the Runge-Kutta method to identify the optimal signalling for covert sensing. As expected and explicitly shown in our numerical results, the identified optimal signalling outperforms both the normal and skew normal distributed signalling, which demonstrates the necessity of optimizing signalling in the context of covert sensing.

While federated learning (FL) improves the generalization of end-to-end autonomous driving by model aggregation, the conventional single-hop FL (SFL) suffers from slow convergence rate due to long-range communications among vehicles and cloud server. Hierarchical federated learning (HFL) overcomes such drawbacks via introduction of mid-point edge servers. However, the orchestration between constrained communication resources and HFL performance becomes an urgent problem. This paper proposes an optimization-based Communication Resource Constrained Hierarchical Federated Learning (CRCHFL) framework to minimize the generalization error of the autonomous driving model using hybrid data and model aggregation. The effectiveness of the proposed CRCHFL is evaluated in the Car Learning to Act (CARLA) simulation platform. Results show that the proposed CRCHFL both accelerates the convergence rate and enhances the generalization of federated learning autonomous driving model. Moreover, under the same communication resource budget, it outperforms the HFL by 10.33% and the SFL by 12.44%.

This paper aims to enhance user fairness in unmanned aerial vehicles (UAVs) enabled cell-free wireless networks. We consider a scenario where multiple UAVs serve as access points for ground users (UEs). We jointly design the UAV deployment, power allocation, UAV-UE association, and pilot assignment to maximize the minimum downlink user rate, while taking account the impact of pilot contamination. The design is formulated as a mixed-integer non-convex optimization problem. To circumvent the problem non-convexity and facilitate the design of a computationally efficient suboptimal solution, a series of transformations and approximations are proposed based on the particle swarm optimization and successive convex approximation techniques. Simulation results are presented to demonstrate the effectiveness and advantages brought by the joint design for enhancing user fairness. Furthermore, our study provides new insights into the feasibility of using sparse association to reduce costs while maintaining performance, especially in scenarios with a large number of users.

The artificial electromagnetic characteristics of reconfigurable intelligent surfaces (RIS) offers new opportunities to increase the secret key rate (SKR) in physical-layer key generation (PKG). However, the existing literature has primarily focused on continuous phase shift designs for RIS to enhance the SKR, while in practice the phase shifts are discrete due to hardware implementations. Hence, the extent to which practical RIS with discrete phase shifts can enhance SKR remains uncertain. Moreover, we have observed that existing optimization methods are not directly applicable to RIS with discrete phase shifts and relying solely on the approximation projection algorithm may lead to certain SKR performance degradation. To address these problems, this paper proposes a RIS-assisted PKG model considering the impact of discrete RIS phase shifts. To maximize the SKR by properly designing the RIS phase shifts, we propose an algorithm utilizing linear conic reformulation (LCR) with alternating difference-of-convex (DC) programming and successive convex approximation (SCA). Simulation results unveil that the proposed LCR-DC algorithm has the capability to achieve the SKR close to the optimal solutions. Furthermore, increasing the number of RIS elements or the number of quantization bits for RIS phase shifts enhances SKR but with diminishing returns. It is worth noting that a small number of discrete phase shifts, e.g., 2-3 quantization bits, is generally sufficient to achieve satisfactory SKR performance.

We investigate the downlink secrecy performance of reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) systems in the presence of a multi-antenna eavesdropper. We first derive a tight closed-form expression for characterizing the lower bound of the achievable ergodic secrecy rate under spatially correlated channels, taking into account low-resolution digital-to-analog converters (DACs) and RIS phase noise. Subsequently, building upon the derived results, we optimize the power allocation among the information signal and artificial noise in closed form and the RIS phase shifts by developing a projected gradient ascent algorithm, which requires only statistical channel state information of the aggregated channel with low implementational complexity. All theoretical analyses and the effectiveness of the proposed algorithm are corroborated by simulation experiments. Our results reveal that low-resolution DAC can be beneficial with equal power allocation when the number of RIS elements is small. Besides, the detrimental influence attributed to low-resolution DACs gains prominence as the number of RIS elements grows substantially.

The integration of sensing and communication enables wireless communication systems to serve environment-aware applications. In this paper, we propose to leverage sensing to enhance physical layer security (PLS) in multiuser communication systems in the presence of a suspicious target. To this end, we develop a two-phase framework to first estimate the location of the potential eavesdropper by sensing and then utilize the estimated information to enhance PLS for communication. In particular, in the first phase, a dual-functional radar and communication (DFRC) base station (BS) exploits a sensing signal to mitigate the sensing information uncertainty of the potential eavesdropper. Then, in the second phase, to facilitate joint sensing and secure communication, the DFRC BS employs beamforming and artificial noise to enhance secure communication. The design objective is to maximize the system sum rate while alleviating the information leakage by jointly optimizing the time allocation and beamforming policy. Capitalizing on monotonic optimization theory, we develop a two-layer globally optimal algorithm to reveal the performance upper bound of the considered system. Simulation results show that the proposed scheme achieves a significant sum rate gain over two baseline schemes that adopt existing techniques. Moreover, our results unveil that ISAC is a promising paradigm for enhancing secure communication in wireless networks.

Multinode communication, which refers to the interaction among multiple devices, has attracted lots of attention in many Internet of Things (IoT) scenarios. However, its huge amounts of data flows and inflexibility for task extension have triggered the urgent requirement of communication-efficient distributed data transmission frameworks. In this article, inspired by the great superiorities on bandwidth reduction and task adaptation of semantic communications, we propose a federated learning (FL)-based semantic communication (FLSC) framework for multitask distributed image transmission with IoT devices. FL enables the design of independent semantic communication link of each user while further improves the semantic extraction and task performance through global aggregation. Each link in FLSC is composed of a hierarchical vision transformer (HVT)-based extractor and a task-adaptive translator for coarse-to-fine semantic extraction and meaning translation according to specific tasks. In order to extend the FLSC into more realistic conditions, we design a channel state information-based multiple-input–multiple-output transmission module to combat channel fading and noise. Simulation results show that the coarse semantic information can deal with a range of image-level tasks. Moreover, especially in low signal-to-noise ratio (SNR) and channel bandwidth ratio regimes, FLSC evidently outperforms the traditional scheme, e.g., about 10 peak SNR gain in the 3-dB channel condition.

Autonomous vehicles (AVs) have become an emerging crucial component of the intelligent transportation system (ITS) in modern smart cities. In particular, coordinated operations of AVs can potentially enhance the quality of public services, e.g., logistic and AV charging services. However, the joint logistic and AV charging scenario involves the sophisticated interactions between a large number of complicated agents, dynamic logistics, and electricity prices in real-world systems. Since AVs are individuals owned by different parties, the design of attractive incentive to motivate them to provide multiple public services becomes a fundamental issue. In this article, we develop an urban intelligent system (UIS) by exploiting the efficient incentive mechanisms, e.g., noncooperative and cooperative game-theoretic approaches, to motivate the AVs to provide logistic and charging services in UIS. For the noncooperative game approach, we formulate the interaction between the selfish AVs and the aggregator as a Stackelberg game. Meanwhile, the aggregator, known as the leader in the game, aims to decide the logistic and electricity trading prices, and then the AVs, executed as the followers, determine their service schedules. Furthermore, considering that all the players are willing to cooperate, we develop a cooperative potential game for the selfless AVs to maximize the social welfare of the UIS. These case studies demonstrate the effectiveness and practicability of proposed incentive mechanisms that can motivate EVs to provide high-quality logistic and charging services by maximizing their utilities. Also, both the proposed schemes provide significant system revenues than that of conventional system optimization-based approaches.

A revolutionary technology relying on Stacked Intelligent Metasurfaces (SIM) is capable of carrying out advanced signal processing directly in the native electromagnetic (EM) wave regime. An SIM is fabricated by a sophisticated amalgam of multiple stacked metasurface layers, which may outperform its single-layer metasurface counterparts, such as reconfigurable intelligent surfaces (RIS) and metasurface lenses. We harness this new SIM for implementing holographic multiple-input multiple-output (HMIMO) communications without requiring excessive radio-frequency (RF) chains, which is a substantial benefit compared to existing implementations. First of all, we propose an HMIMO communication system based on a pair of SIM at the transmitter (TX) and receiver (RX), respectively. In sharp contrast to the conventional MIMO designs, SIM is capable of automatically accomplishing transmit precoding and receiver combining, as the EM waves propagate through them. As such, each spatial stream can be directly radiated and recovered from the corresponding transmit and receive port. Secondly, we formulate the problem of minimizing the error between the actual end-to-end channel matrix and the target diagonal one, representing a flawless interference-free system of parallel subchannels. This is achieved by jointly optimizing the phase shifts associated with all the metasurface layers of both the TX-SIM and RX-SIM. We then design a gradient descent algorithm to solve the resultant non-convex problem. Furthermore, we theoretically analyze the HMIMO channel capacity bound and provide some fundamental insights. Finally, extensive simulation results are provided for characterizing our SIM-aided HMIMO system, which quantifies its substantial performance benefits, e.g., 150% capacity improvement over both conventional MIMO and its RIS-aided counterparts.

Beamforming design has been widely investigated for integrated sensing and communication (ISAC) systems with full-duplex (FD) sensing and half-duplex (HD) communication, where the base station (BS) transmits and receives radar sensing signals simultaneously while the integrated communication operates in either downlink or uplink. To achieve higher spectral efficiency, in this paper, we extend existing ISAC beamforming design to a general case by considering the FD capability for both radar and communication. Specifically, we consider an FD ISAC system, where the BS performs target detection and communicates with multiple downlink users and uplink users reusing the same time and frequency resources. We jointly optimize the downlink dual-functional transmit signal and the uplink receive beamformers at the BS and the transmit power at the uplink users. The problems are formulated under two criteria: power consumption minimization and sum rate maximization. The downlink and uplink transmissions are tightly coupled due to both the desired target echo and the undesired interference received at the BS, making the problems challenging. To handle these issues in both cases, we first determine the optimal receive beamformers in closed forms with respect to the BS transmit beamforming and the user transmit power. Subsequently, we invoke these results to obtain equivalent optimization problems and propose iterative algorithms to solve them. In addition, we consider a special case under the power minimization criterion and propose an alternative low complexity design. Numerical results demonstrate that the optimized FD communication-based ISAC brings tremendous improvements in terms of both power efficiency and spectral efficiency compared to the conventional ISAC with HD communication.

This paper proposes a unified semi-blind detection framework for sourced and unsourced random access (RA), which enables next-generation ultra-reliable low-latency communications (URLLC) with a massive number of devices. Specifically, the active devices transmit their uplink access signals in a grant-free manner to realize ultra-low access latency. Meanwhile, the base station aims to achieve ultra-reliable data detection under severe inter-device interference without exploiting explicit channel state information (CSI). We first propose an efficient transmitter design, where a small amount of reference information (RI) is embedded in the access signal to resolve the inherent ambiguities incurred by the unknown CSI. At the receiver, we further develop a successive interference cancellation-based semi-blind detection scheme, where a bilinear generalized approximate message passing algorithm is utilized for joint channel and signal estimation (JCSE), while the embedded RI is exploited for ambiguity elimination. Particularly, a rank selection approach and a RI-aided initialization strategy are incorporated to reduce the algorithmic computational complexity and to enhance the JCSE reliability, respectively. Besides, four enabling techniques are integrated to satisfy the stringent latency and reliability requirements of massive URLLC. Numerical results demonstrate that the proposed semi-blind detection framework offers a better scalability-latency-reliability tradeoff than the state-of-the-art detection schemes dedicated to sourced or unsourced RA.

This paper investigates the orthogonal time frequency space (OTFS) transmission for enabling ultra-reliable low-latency communications (URLLC). To guarantee excellent reliability performance, pragmatic precoder design is an effective and indispensable solution. However, the design requires accurate instantaneous channel state information at the transmitter (ICSIT) which is not always available in practice. Motivated by this, we adopt a deep learning (DL) approach to exploit implicit features from estimated historical delay-Doppler domain channels (DDCs) to directly predict the precoder to be adopted in the next time frame for minimizing the frame error rate (FER), that can further improve the system reliability without the acquisition of ICSIT. To this end, we first establish a predictive transmission protocol and formulate a general problem for the precoder design where a closed-form theoretical FER expression is derived serving as the objective function to characterize the system reliability. Then, we propose a DL-based predictive precoder design framework which exploits an unsupervised learning mechanism to improve the practicability of the proposed scheme. As a realization of the proposed framework, we design a DDCs-aware convolutional long short-term memory (CLSTM) network for the precoder design, where both the convolutional neural network and LSTM modules are adopted to facilitate the spatial-temporal feature extraction from the estimated historical DDCs to further enhance the precoder performance. Simulation results demonstrate that the proposed scheme facilitates a flexible reliability-latency tradeoff and achieves an excellent FER performance that approaches the lower bound obtained by a genie-aided benchmark requiring perfect ICSI at both the transmitter and receiver.

Cell-free (CF) massive multiple-input multiple-output (MIMO) and reconfigurable intelligent surface (RIS) are two promising technologies for realizing future beyond-fifth generation (B5G) networks. In this paper, we consider a practical spatially correlated RIS-aided CF massive MIMO system with multi-antenna access points (APs) over spatially correlated fading channels. Different from previous work, the electromagnetic interference (EMI) at RIS is considered to further characterize the system performance of the actual environment. Then, we derive the closed-form expression for the system spectral efficiency (SE) with the maximum ratio (MR) combining at the APs and the large-scale fading decoding (LSFD) at the central processing unit (CPU). Moreover, to counteract the near-far effect and EMI, we propose practical fractional power control (FPC) and max-min power control algorithms to further improve the system performance. We unveil the impact of EMI, channel correlations, and different signal processing methods on the uplink SE of user equipments (UEs). The accuracy of our derived analytical results is verified by extensive Monte-Carlo simulations. Our results show that the EMI can substantially degrade the SE, especially for those UEs with unsatisfactory channel conditions. Besides, increasing the number of RIS elements is always beneficial in terms of the SE, but with diminishing returns when the number of RIS elements is sufficiently large. Furthermore, the existence of spatial correlations among RIS elements can deteriorate the system performance when RIS is impaired by EMI.

Reconfigurable intelligent surface (RIS) can significantly enhance the service coverage of Tera-Hertz massive multiple-input multiple-output (MIMO) communication systems. However, obtaining accurate high-dimensional channel state information (CSI) with limited pilot and feedback signaling overhead is challenging, severely degrading the performance of conventional spatial division multiple access. To improve the robustness against CSI imperfection, this paper proposes a deep learning (DL)-based rate-splitting multiple access (RSMA) scheme for RIS-aided Tera-Hertz multi-user MIMO systems. Specifically, we first propose a hybrid data-model driven DL-based RSMA precoding scheme, including the passive precoding at the RIS as well as the analog active precoding and the RSMA digital active precoding at the base station (BS). To realize the passive precoding at the RIS, we propose a Transformer-based data-driven RIS reflecting network (RRN). As for the analog active precoding at the BS, we propose a match-filter based analog precoding scheme considering that the BS and RIS adopt the LoS-MIMO antenna array architecture. As for the RSMA digital active precoding at the BS, we propose a low-complexity approximate weighted minimum mean square error (AWMMSE) digital precoding scheme, and further design a model-driven deep unfolding active precoding network (DFAPN) by combining the proposed AWMMSE scheme with DL. Then, to acquire accurate CSI at the BS for the investigated RSMA precoding scheme to achieve higher spectral efficiency, we propose a CSI acquisition network (CAN) with low pilot and feedback signaling overhead. The proposed DL-based RSMA scheme for RIS-aided Tera-Hertz multi-user MIMO systems can exploit the advantages of RSMA and DL to improve the robustness against CSI imperfection, thus achieving higher spectral efficiency with lower signaling overhead.

This paper investigates reconfigurable intelligent surface (RIS)-assisted simultaneous wireless information and power transfer (SWIPT) networks with rate splitting multiple access (RSMA). An energy efficiency (EE) maximization problem is formulated subject to the power budget at the transmitter and the quality of service (QoS) requirements of both information communication and energy harvesting, where the beamforming vectors, the power splitting (PS) ratios, the common message rates, and the discrete phase shifts are jointly optimized. To tackle the non-convex problem with both discrete and continuous variables, a deep reinforcement learning-based approach is proposed with the proximal policy optimization (PPO) framework. Different from traditional optimization approaches which optimizes the beamforming vectors and phase shifts separately and alternatively, our proposed PPO-based approach optimizes all the variables in unison. Besides, to perform beamforming design in action space, the beamforming vectors for the common stream and the private stream are respectively designed based on the maximum-ratio transmission and the zero forcing to enhance both energy and information transmission. To evaluate the performance of the PPO-based approach, a successive convex approximation (SCA) and Dinkelbach’s method based solution scheme (named SCA-D scheme) is also presented. Simulation results show that the system EE obtained by the proposed PPO-based approach is close to that obtained by the SCA-D scheme while outperforming various benchmarks. The RSMA contributes to the EE of the system greatly compared with traditional scheme. As for the case of time-varying channels, the proposed PPO-based approach is with much smaller running time by only sacrificing a slight EE performance compared with the SCA-D scheme.

In practical cell-free (CF) massive multiple-input multiple-output (MIMO) networks with distributed and low-cost access points, the asynchronous arrival of signals at the user equipments increases multi-user interference that degrades the system performance. Meanwhile, rate-splitting (RS), exploiting the transmission of both common and private messages, has demonstrated to offer considerable spectral efficiency (SE) improvements and its robustness against channel state information (CSI) imperfection. The signal performance of a CF massive MIMO system is first analyzed for asynchronous reception capturing the joint effects of propagation delays and oscillator phases of transceivers. Taking into account the imperfect CSI caused by asynchronous phases and pilot contamination, we derive novel and closed-form downlink SE expressions for characterizing the performance of both the RS-assisted and conventional non-RS-based systems adopting coherent and non-coherent data transmission schemes, respectively. Moreover, we formulate the design of robust precoding for the common messages as an optimization problem that maximizes the minimum individual SE of the common message. To address the non-convexity of the design problem, a bisection method is proposed to solve the problem optimally. Simulation results show that asynchronous reception indeed destroys both the orthogonality of the pilots and the coherent data transmission resulting in poor system performance. Besides, thanks to the uniform coverage properties of CF massive MIMO systems, RS with a simple low-complexity precoding for the common message obtained by the equal ratio sum of the private precoding is able to achieve substantial downlink sum SE gains, while the application of robust precoding to the common message is shown to be useful in some extreme cases, e.g., serious oscillator mismatch and unknown delay phase.

Physical-layer key generation (PKG) exploits the reciprocity and randomness of wireless channels to generate a symmetric key between two legitimate communication ends. However, in multi-cell systems, PKG suffers from severe pilot contamination due to the reuse of pilots in different cells. In this paper, we invoke multiple reconfigurable intelligent surfaces (RISs) for adaptively shaping the environment and enhancing the PKG performance. To this end, we formulate an optimization problem to maximize the weighted sum key rate (WSKR) by jointly optimizing the precoding matrices at the base stations (BSs) and the phase shifts at the RISs. To address the non-convexity of the problem, we adopt an alternating optimization (AO)-based algorithm that divides the joint optimization problem into two subproblems. For the subproblem of precoding matrices, we apply the Lagrangian dual approach based on the Karush-Kuhn-Tucker (KKT) conditions. As for the subproblem of phase shifts, we adopt a projected gradient ascent (PGA) algorithm. Simulation results validate the effectiveness of the proposed scheme, demonstrating significant gains in WSKR. Moreover, compared with a single-RIS case, deploying multiple RISs offer spatial diversity so as to improve the PKG performance of multicell systems.

This paper studies a downlink secure integrated sensing and communication (ISAC) system, in which a multi-antenna base station (BS) transmits confidential messages to a single-antenna communication user (CU) while performing sensing on targets that may act as suspicious eavesdroppers. To ensure the quality of target sensing while preventing their potential eavesdropping, the BS combines the transmit confidential information signals with additional dedicated sensing signals, which play a dual role of artificial noise (AN) for degrading the qualities of eavesdropping channels. Under this setup, we jointly design the transmit information and sensing beamforming, with the objective of minimizing the weighted sum of beampattern matching errors and cross-correlation patterns for sensing subject to secure communication constraints. The robust design takes into account the channel state information (CSI) imperfectness of the eavesdroppers in two practical CSI error scenarios. First, we consider the scenario with bounded CSI errors of eavesdroppers, in which the worst-case secrecy rate constraint is adopted to ensure secure communication performance. In this scenario, we present the optimal solution to the worst-case secrecy rate constrained sensing beampattern optimization problem, by adopting the techniques of S-procedure, semi-definite relaxation (SDR), and a one-dimensional (1D) search, for which the tightness of the SDR is rigorously proved. Next, we consider the scenario with Gaussian CSI errors of eavesdroppers, in which the secrecy outage probability constraint is adopted. In this scenario, we present an efficient algorithm to solve the more challenging secrecy outage-constrained sensing beampattern optimization problem, by exploiting the convex restriction technique based on the Bernstein-type inequality, together with the SDR and 1D search. Finally, numerical results show that the proposed designs can properly adjust the information and sensing beams to balance the tradeoffs among communicating with CU, sensing targets, and confusing eavesdroppers, so as to achieve desirable sensing transmit beampatterns while ensuring the CU’s secrecy requirements for the two scenarios.

Reconfigurable intelligent surface (RIS)-aided terahertz (THz) communication has been considered as a promising technology for enabling future sixth-generation (6G) wireless systems. Due to the exploitation of extremely large bandwidth and large scale of RIS, RIS-aided THz communications would suffer from the beam split effect, where the generated beams cannot be aligned with the target physical direction in the whole bandwidth, so a severe array gain loss will be introduced. In this paper, the beam split effect is first analyzed in the existence of RIS. Then, a novel sub-connected RIS architecture is proposed to mitigate the beam split effect. The crux is to introduce additional time-delay (TD) modules and phase shifters into RIS elements so as to convert the classical phase-only precoding to the joint phase and delay precoding. Accordingly, a wideband precoding design is proposed to compensate for the severe array gain loss, and the performance analysis on the array gain is also provided. After that, we extend our discussions to the emerging scenarios with massive antennas equipped at the base station (BS), where the effect of “double beam split”, i.e., the coupling of beam split at the BS and the RIS, occurs. We prove the decomposability of the array gain, based on which the double beam split effect can be addressed by separately optimizing the wideband precoding at the BS and the RIS. Simulation results demonstrate that our proposed sub-connected RIS significantly alleviates the beam split effect with a small number of TD modules, and it is capable of achieving sub-optimal achievable rate performance with acceptable hardware cost and power consumption.

In this paper, rapidly converging low-complexity iterative transmit precoding (TPC) techniques are proposed for the massive multiple-input multiple-output (MIMO) downlink. First of all, the proposed random block-based iterative TPC (RBI-TPC) algorithm performs its iterations by updating multiple rather than a single component at each instant, where the updating order of each block containing multiple components relies on the samples randomly sampled from a discrete distribution. Based on the analytically derived convergence rate, we demonstrate that improved convergence is achieved by the block-based update mechanism conceived since the correlation between multiple components can be beneficially exploited. Then, the random sampling that determines the updating order is studied. By applying conditional random sampling, the updating order is optimized based on the latest updates for attaining more rapid convergence. We also demonstrate that the associated updating order may become deterministic under specific conditions so that a fixed but optimized updating order can be used for facilitating the practical implementations, which paves the way for conceiving the ordered block-based iterative TPC (OBI-TPC) algorithm. Finally, the concept of successive over-relaxation (SOR) is adopted for further convergence improvement and simulations are presented to illustrate the performance improvements of the proposed RBI and OBI TPC algorithms compared to the existing low-complexity iterative TPC schemes.

Deep learning (DL) applied to a device’s radio-frequency fingerprint (RFF) has attracted significant attention in physical-layer authentication due to its extraordinary classification performance. Conventional DL-RFF techniques are trained by adopting maximum likelihood estimation (MLE). Although their discriminability has recently been extended to unknown devices in open-set scenarios, they still tend to overfit the channel statistics embedded in the training dataset. This restricts their practical applications as it is challenging to collect sufficient training data capturing the characteristics of all possible wireless channel environments. To address this challenge, we propose a DL framework of disentangled representation (DR) learning that first learns to factor the signals into a device-relevant component and a device-irrelevant component via adversarial learning. Then, it shuffles these two parts within a dataset for implicit data augmentation, which imposes a strong regularization on RFF extractor learning to avoid the possible overfitting of device-irrelevant channel statistics, without collecting additional data from unknown channels. Experiments validate that the proposed approach, referred to as DR-based RFF, outperforms conventional methods in terms of generalizability to unknown devices under unknown complicated propagation environments, e.g., dispersive multipath fading channels, even though all the training data are collected in a simple environment with dominated direct line-of-sight (LoS) propagation paths.

In this paper, we consider the robust beamforming design in a reconfigurable intelligent surface (RIS)-aided cell-free (CF) system considering the channel state information (CSI) uncertainties of both the direct channels and cascaded channels at the transmitter with capacity-limited backhaul. We jointly optimize the precoding at the access points (APs) and the phase shifts at multiple RISs to maximize the worst-case sum rate of the CF system subject to the constraints of maximum transmit power of APs, unit-modulus phase shifts, limited backhaul capacity, and bounded CSI errors. By applying a series of transformations, the non-smoothness and semi-infinite constraints are tackled in a low-complexity manner that facilitates the design of an alternating optimization (AO)-based iterative algorithm. The proposed algorithm divides the considered problem into two subproblems. For the RIS phase shifts optimization subproblem, we exploit the penalty convex-concave procedure (P-CCP) to obtain a stationary solution and achieve effective initialization. For precoding optimization subproblem, successive convex approximation (SCA) is adopted with a convergence guarantee to a Karush-Kuhn-Tucker (KKT) solution. Numerical results demonstrate the effectiveness of the proposed robust beamforming design, which achieves superior performance with low complexity. Moreover, the importance of RIS phase shift optimization for robustness and the advantages of distributed RISs in the CF system are further highlighted.

Due to the ability of feature extraction, deep learning (DL)-based methods have been recently applied to channel state information (CSI) compression feedback in massive multiple-input multiple-output (MIMO) systems. Existing DL-based CSI compression methods are usually effective in extracting a certain type of features in the CSI. However, the CSI usually contains two types of propagation features, i.g., non-line-of-sight (NLOS) propagation-path feature and dominant propagation-path feature, especially in channel environments with rich scatterers. To fully extract the both propagation features and learn a dual-feature representation for CSI, this paper proposes a dual-feature-fusion neural network (NN), referred to as DuffinNet. The proposed DuffinNet adopts a parallel structure with a convolutional neural network (CNN) and an attention-empowered neural network (ANN) to respectively extract different features in the CSI, and then explores their interplay by a fusion NN. Built upon this proposed DuffinNet, a new encoder-decoder framework is developed, referred to as Duffin-CsiNet, for improving the end-to-end performance of CSI compression and reconstruction. To facilitate the application of Duffin-CsiNet in practice, this paper also presents a two-stage approach for codeword quantization of the CSI feedback. Besides, a transfer learning-based strategy is introduced to improve the generalization of Duffin-CsiNet, which enables the network to be applied to new propagation environments. Simulation results illustrate that the proposed Duffin-CsiNet noticeably outperforms the existing DL-based methods in terms of reconstruction performance, encoder complexity, and network convergence, validating the effectiveness of the proposed dual-feature fusion design.

This paper investigates a joint transmissive and reflective reconfigurable intelligent surfaces (RIS) -aided secure multiple-input multiple-output (MIMO) system, where both a RIS-assisted transmitter and a RIS-based reflector are deployed to defend against the simultaneous jamming attack and wiretapping threat. Our design focuses on maximizing the sum rate under the unknown jammer’s beamforming, joint RISs’ coupled phase shift errors (PSEs), and spatially-correlated angular channel uncertainties. Besides, we take into account the various quality-of-service (QoS) requirement constraints for guaranteeing the secure performance. Since the problem is non-convex and mathematically intractable, a new optimization framework is established to facilitate the solution development to the formulated problem. Specifically, armed with the Akaike information criterion, a novel diagonalization method is first proposed to estimate the unknown jamming covariance matrix. Then, a series of fractional-eliminated rate expressions is derived that facilitates the application of the proposed Double Deterministic Transformation (DDT) to tackle the coupled stochastic PSEs. Besides, regardless of the spatial correlation matrix, a general discretization method is proposed to convert the e spatially-correlatd angular uncertainties into a worst-case robust one. Subsequently, building upon the above transformations which transform the original problem into tractable one, a two-layer iterative Lagrange multiplier algorithm capitalizing a low-complexity dual method is proposed to obtain the globally optimal solution of the digital precoder, where the multiple QoS constraints are handled without iteration. Meanwhile, we develop a novel polyblock-based multiple penalty method to obtain the globally optimal solutions to RISs’ phase shifts which can simultaneously satisfy the multiple QoS constraints. Moreover, to address the narrow feasibility region induced by the multiple QoS constraints, a heuristic initial optimization method is proposed, which strengthens the existing result. Finally, theoretical analysis and numerical results demonstrate the optimality and the excellent performance of our proposed optimization framework.

Considering a simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS)-aided dual-functional radar-communications (DFRC) system, this paper proposes a symbol-level precoding-based scheme for concurrent securing confidential information transmission and performing target sensing, where the public signals intended for multiple unclassified users are exploited to deceive the multiple potential malicious radar targets. Specifically, the STAR-RIS-aided DFRC system design is formulated as a joint optimization problem that determines the transmission waveform signal, the transmission and reflection coefficients of STAR-RIS. The objective is to maximize the average received radar sensing power subject to the quality-of-service constraints for multiple communication users, the security constraint for multiple potential eavesdroppers, as well as various practical waveform design restrictions. However, the formulated problem is challenging to handle due to its nonconvexity. Furthermore, the high dimensionality of the optimization variables also renders existing optimization algorithms inefficient. To address these issues, we propose a distance-majorization induced low-complexity algorithm to obtain an efficient solution, which converts the nonconvex joint design problem into a sequence of subproblems that can be solved in closed-form, relieving the required high computational burden of the conventional approaches, e.g., the interior point method. Simulation results confirm the effectiveness of the STAR-RIS in improving the DFRC performance. Besides, by comparing with the state-of-the-art alternating direction method of multipliers (ADMM) algorithm, simulation results validate the efficiency of our proposed optimization algorithm and show that it enjoys excellent scalability for different number of T-R elements equipped at the STAR-RIS.

Integrating sensing functionalities is envisioned as a distinguishing feature of next-generation mobile networks, which has given rise to the development of a novel enabling technology– Integrated Sensing and Communication (ISAC). Portraying the theoretical performance bounds of ISAC systems is fundamentally important to understand how sensing and communication functionalities interact (e.g., competitively or cooperatively) in terms of resource utilization, while revealing insights and guidelines for the development of effective physical-layer techniques. In this paper, we characterize the fundamental performance tradeoff between the detection probability for target monitoring and the user’s achievable rate in ISAC systems. To this end, we first discuss the achievable rate of the user under sensing-free and sensing-interfered communication scenarios. Furthermore, we derive closed-form expressions for the probability of false alarm (PFA) and the successful probability of detection (PD) for monitoring the target of interest, where we consider both communication-assisted and communication-interfered sensing scenarios. In addition, the effects of the unknown channel coefficient are also taken into account in our theoretical analysis. Based on our analytical results, we then carry out a comprehensive assessment of the performance tradeoff between sensing and communication functionalities. Specifically, we formulate a power allocation problem to minimize the transmit power at the base station (BS) under the constraints of ensuring a required PD for perception as well as the communication user’s quality of service requirement in terms of achievable rate. It indicates that, on the one hand, there exists an intrinsic tradeoff between sensing and communication performance under the mutual-interfered scenarios; On the other hand, with prior knowledge of the baseband waveform, these two functionalities might mutually assist each other to enhance the performance. Finally, simulation results corroborate the accuracy of our theoretical analysis and the effectiveness of the proposed power allocation solutions showing the advantages of the ISAC system over the conventional radar and communication coexistence counterpart.

Anintelligent reflecting surface (IRS)-aided wireless-powered mobile edge computing (WP-MEC) system is conceived, where each device’s computational task can be divided into two parts for local computing and offloading to mobile edge computing (MEC) servers, respectively. Both time division multiple access (TDMA) and non-orthogonal multiple access (NOMA) schemes are considered for uplink (UL) offloading. To fully unleash the potential benefits of the IRS, employing multiple IRS beamforming (BF) patterns/vectors in the considered operating frame to create time-selectivity channels, i.e., dynamic IRS BF (DIBF), is in principle possible at the cost of additional signaling overhead. To strike a balance between the system performance and associated signalling overhead, we propose three cases of DIBF configurations based on the maximum number of IRS reconfiguration times. The degree-of-freedom provided by the IRS may introduce different impacts on the TDMA and NOMA-based UL offloading schemes. Thus, it is still fundamentally unknown which multiple access scheme is superior for MEC UL offloading by considering the impact of the IRS. To answer this question, we provide a comprehensively theoretical performance comparison for the TDMA and NOMA-based offloading schemes under the three cases of DIBF configurations by characterizing their achievable computation rate. Analytical results demonstrate that offloading adopting TDMA can achieve the same computation rate as that of NOMA, when all the devices share the same IRS BF vector during the UL offloading. By contrast, computation offloading exploiting TDMA outperforms NOMA, when the IRS BF vector can be flexibly adapted for UL offloading. Then, we propose computationally efficient algorithms by invoking alternating optimization for solving their associated computation rate maximization problems. Our numerical results demonstrate the significant performance gains achieved by the proposed designs over various benchmark schemes and also unveil that the optimal time allocated to downlink wireless power transfer can be effectively reduced with the aid of IRSs, which is beneficial for both the system’s spectral efficiency and its energy efficiency.

Active intelligent reflecting surface (IRS) has been recently proposed to alleviate the product path loss attenuation inherent in the IRS-aided cascaded channel. In this paper, we study an active IRS-aided simultaneous wireless information and power transfer (SWIPT) system. Specifically, an active IRS is deployed to assist a multi-antenna access point (AP) to convey information and energy simultaneously to multiple single-antenna information users (IUs) and energy users (EUs). Two joint transmit and reflect beamforming optimization problems are investigated with different practical objectives. The first problem maximizes the weighted sum-power harvested by the EUs subject to individual signal-to-interference-plus-noise ratio (SINR) constraints at the IUs, while the second problem maximizes the weighted sum-rate of the IUs subject to individual energy harvesting (EH) constraints at the EUs. The optimization problems are non-convex and difficult to solve optimally. To tackle these two problems, we first rigorously prove that dedicated energy beams are not required for their corresponding semidefinite relaxation (SDR) reformulations and the SDR is tight for the first problem, thus greatly simplifying the AP precoding design. Then, by capitalizing on the techniques of alternating optimization (AO), SDR, and successive convex approximation (SCA), computationally efficient algorithms are developed to obtain suboptimal solutions of the resulting optimization problems. Simulation results demonstrate that, given the same total system power budget, significant performance gains in terms of operating range of wireless power transfer (WPT), total harvested energy, as well as achievable rate can be obtained by our proposed designs over benchmark schemes (especially the one adopting a passive IRS). Moreover, it is advisable to deploy an active IRS in the proximity of the users for the effective operation of WPT/SWIPT.

Integrated sensing and communication (ISAC) has opened up numerous game-changing opportunities for realizing future wireless systems. In this paper, we propose an ISAC processing framework relying on millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. Specifically, we provide a compressed sampling (CS) perspective to facilitate ISAC processing, which can not only recover the high-dimensional channel state information or/and radar imaging information, but also significantly reduce pilot overhead. First, an energy-efficient widely spaced array (WSA) architecture is tailored for the radar receiver, which enhances the angular resolution of radar sensing at the cost of angular ambiguity. Then, we propose an ISAC frame structure for time-varying ISAC systems considering different timescales. The pilot waveforms are judiciously designed by taking into account both CS theories and hardware constraints induced by hybrid beamforming (HBF) architecture. Next, we design the dedicated dictionary for WSA that serves as a building block for formulating the ISAC processing as sparse signal recovery problems. The orthogonal matching pursuit with support refinement (OMP-SR) algorithm is proposed to effectively solve the problems in the existence of the angular ambiguity. We also provide a framework for estimating the Doppler frequencies during payload data transmission to guarantee communication performances. Simulation results demonstrate the good performances of both communications and radar sensing under the proposed ISAC framework.

In this work, we investigate the status packet covert communication with time uncertainty in time-critical wireless networks. We model the packet generation as a Poisson process that concatenates the information timeliness and communication covertness, since the prior transmission probability is highly correlated with the packet generation. To balance between the timeliness and covertness of the status packet transmission, we propose two schemes, named random sub-slot selection (RSS) scheme and random channel use selection (RCUS) scheme, by exploiting the random transmission time to confuse a warden Willie’s binary detection on the covert communication. Subsequently, the average age of information (AoI) subject to the covertness constraint is derived for the proposed RSS and RCUS schemes. It is demonstrated that the symbol-length of the status packet introduces a non-trivial tradeoff between the covertness and timeliness. Inspired by this, further designs of the symbol-length and the transmit power for the proposed two schemes are formulated as optimization problems and solved optimally. Our numerical results demonstrate the superiority of proposed schemes over the existing covert communication strategies. In addition, our examination reveals that the RCUS scheme outperforms the RSS scheme when the covertness constraint is extremely strict. Otherwise, the RSS scheme generally outperforms the RCUS scheme in terms of achieving a lower AoI.

As a way of providing users flexible computing services, networks exist that can make full use of air and ground computing resources. Such networks are called air-ground collaborative mobile edge computing (AGC-MEC) networks. AGC-MEC supports numerous emerging real-time applications for which timely computed results are critical. Researchers have developed a novel metric “age of information (AoI)” that can capture the freshness of computed results. This is the first paper to study the problem of AoI-aware scheduling forAir-groundCollaborative mobileEdge computing (i.e., IACE). So as to minimize the weighted AoI of all the terrestrial user equipments (UEs), we have jointly optimized task scheduling, computing resource allocation, and unmanned aerial vehicle (UAV) trajectory taking into account the constraints on the computing resources and the available energy of the UAV. The formulated problem, which is a challenge to solve, is a mixed-integer nonlinear programming (MINLP) problem. To obtain an effective solution, we propose an iterative algorithm based on the alternating optimization approach, which entails dividing the considered problem into three subproblems. Extensive simulations show that the proposed algorithm can achieve lower weighted AoI than five benchmark algorithms, while satisfying the resource constraints. Furthermore, simulation results demonstrate two interesting insights. First, the introduction of an aerial MEC server facilitates a flexible offloading design of the UEs which is critical to guaranteeing the freshness of computed results. Second, by optimizing the scheduling, the proposed design can unlock performance gains, especially in the resource-limited regime.

This paper studies the robust resource allocation design for secure intelligent reflecting surface (IRS)-aided wireless powered communication networks (WPCN). In particular, deploying an IRS can establish favorable end-to-end radio propagation environment for achieving the desired performance gain in secure wireless-powered systems. We aim to minimize the total hybrid base station (HBS) transmit power by jointly designing the active transmitting and receiving beamforming at the HBS, the passive beamforming at the IRS, and the transmit power of each wireless-powered device (WD) and jammer node (JN). We formulate a non-convex optimization problem for the robust resource allocation design taking into account the secrecy rate requirement of the WDs and the power budgets for both the WDs and the JNs. To handle this intractable problem, we propose a computationally efficient iterative suboptimal algorithm exploiting the block coordinate descent approach, the successive convex approximation, and the penalty method, which attains a Karush-Kuhn-Tucker (KKT) solution of the transformed problem. Also, we reveal that the optimal energy beamforming matrices are rank-one sharing the same spatial direction. Simulation results unveil that the proposed scheme is able to dramatically reduce the HBS transmit power over various baseline schemes adopting existing solutions. Besides, our results show the superiority of introducing IRS for secure communication in wireless-powered systems.

This work investigates the covert information freshness in intelligent reflecting surface (IRS)-aided communications, where a public full-duplex user (Alice) and a private full-duplex user (Bob) exchange information in the presence of a watchful warden (Willie). In particular, with the help of Alice’s undisguised signal transmission, Bob can establish covert communications such that his transmission can be shielded from Willie. Considering both the non-retransmission protocol and the automatic repeat-request (ARQ) protocol for Bob’s transmission, we study the resource allocation design. By exploiting the channel statistics, the joint design of active beamforming at Alice and Bob, the passive beamforming at the IRS, and the packet length of the confidential data packet is formulated as a nonconvex optimization problem which minimizes the age of information (AoI) at Alice for the two considered protocols taking into account the quality of service in terms of the maximum tolerable AoI at Bob and communication covertness. To circumvent the non-convexity of the design problem, we propose alternating optimization algorithms to find effective solutions. Numerical results demonstrate the superiority of our proposed optimization algorithms over various benchmarks and unveil the decrease of the optimized packet length with the improved covert channel quality.

Cognitive radio (CR) is one of the most disruptive techniques for enabling the next generation wireless communication networks due to its potential in improving the spectral efficiency. In this paper, intelligent reflecting surface (IRS) is exploited to enhance both the accuracy of spectrum sensing and the secondary transmission in a CR network (CRN) employing the opportunistic spectrum access. A novel detection threshold based on the probability of false alarm is derived for improving the spectrum sensing performance. The average achievable rate of the secondary network is maximized under both the two-stage and one-stage IRS phase shifts case. To tackle the challenging non-convex optimization problem under the two-stage case, a computationally efficient block coordinate descent (BCD)-based algorithm is proposed coputilizing the techniques of successive convex approximation (SCA) and semidefinite relaxation (SDR). Moreover, a BCD method and a tractable approximation of the probability of detection are exploited to tackle the problem under one-stage IRS phase shifts case. Simulation results demonstrate that our proposed designs are superior to the benchmark schemes in terms of the achievable rate and the sensing performance, and IRS can greatly improve the spectral efficiency of the CRN.

Cell-free massive MIMO (CF mMIMO) is a promising next generation wireless architecture to realize federated learning (FL). However, sensitive information of user equipments (UEs) may be exposed to the involved access points or the central processing unit in practice. To guarantee data privacy, effective privacy-preserving mechanisms are defined in this paper. In particular, we demonstrate and characterize the possibility in exploiting the inherent quantization error, caused by low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs), for privacy-preserving in a FL CF mMIMO system. Furthermore, to reduce the required uplink training time in such a system, a stochastic non-convex design problem that jointly optimizing the transmit power and the data rate is formulated. To address the problem at hand, we propose a novel power control method by utilizing the successive convex approximation approach to obtain a suboptimal solution. Besides, an asynchronous protocol is established for mitigating the straggler effect to facilitate FL. Numerical results show that compared with the conventional full power transmission, adopting the proposed power control method can effectively reduce the uplink training time under various practical system settings. Also, our results unveil that our proposed asynchronous approach can reduce the waiting time at the central processing unit for receiving all user information, as there are no stragglers that requires a long time to report their local updates.

This paper investigates a novel method for designing linear precoders with finite alphabet inputs based on autoencoders (AE) without the knowledge of the channel model. By model-free training of the autoencoder in a multiple-input multiple-output (MIMO) system, the proposed method can effectively solve the optimization problem to design the precoders that maximize the mutual information between the channel inputs and outputs, when only the input-output information of the channel can be observed. Specifically, the proposed method regards the receiver and the precoder as two independent parameterized functions in the AE and alternately trains them using the exact and approximated gradient, respectively. Compared with previous precoders design methods, it alleviates the limitation of requiring the explicit channel model to be known. Simulation results show that the proposed method works as well as those methods under known channel models in terms of maximizing the mutual information and reducing the bit error rate.

Reconfigurable intelligent surface (RIS) is a promising technique to enhance the performance of physical-layer key generation (PKG) due to its ability to smartly customize the radio environments. Existing RIS-assisted PKG methods are mainly based on the idealistic assumption of an independent and identically distributed (i.i.d.) channel model at both the transmitter and the RIS. However, the i.i.d. model is inaccurate for a typical RIS in an isotropic scattering environment. Also, neglecting the existence of channel spatial correlation would degrade the PKG performance. In this paper, we establish a general spatially correlated channel model in multi-antenna systems and propose a new PKG framework based on the transmit and the reflective beamforming at the base station (BS) and the RIS. Specifically, we derive a closed-form expression for characterizing the key generation rate (KGR) and obtain a globally optimal solution of the beamformers to maximize the KGR. Furthermore, we analyze the KGR performance difference between the one adopting the assumption of the i.i.d. model and that of the spatially correlated model. It is found that the beamforming designed for the correlated model outperforms that for the i.i.d. model while the KGR gain increases with the channel correlation. Simulation results show that compared to existing methods based on the i.i.d. fading model, our proposed method achieves about 5 dB performance gain when the BS antenna correlation$\rho$is 0.3 and the RIS element spacing is half of the wavelength.

In this paper, we investigate a cell-free massive multiple-input multiple-output (CF mMIMO) system with both multi-antenna user equipments (UEs) and access points (APs) over spatially correlated Rayleigh fading channels. In practi-cal CF mMIMO systems, the in-phase and quadrature-phase imbalance (IQI) and low-resolution analog-to-digital converters (ADCs) at the APs are critical for the system performance. Taking these factors into account, the achievable uplink spectral efficiency (SE) is analyzed based on a two-layer decoding scheme. In particular, the maximum ratio (MR) and local minimum mean-square error (L-MMSE) combining are adopted at the APs while the large-scale fading decoding (LSFD) is implemented at the central processing unit (CPU). Furthermore, we derive novel closed-form SE expressions with the MR combining and investigate the SE performance for different combining schemes, quantization bits, and IQI parameters. Numerical results reveal the performance degradations caused by both the low-resolution ADCs and IQI. Additionally, increasing the number of APs is an effective means to promote the system performance.

We consider a practical spatially correlated recon-figurable intelligent surface (RIS)-aided cell-free (CF) massive multiple-input-multiple-output (mMIMO) system with multi-antenna access points (APs) over spatially correlated Rician fading channels. The minimum mean square error (MMSE) channel estimator is adopted to estimate the aggregated RIS channels. Then, we investigate the uplink spectral efficiency (SE) with the maximum ratio (MR) and the local minimum mean squared error (L-MMSE) combining at the APs and obtain the closed-form expression for characterizing the performance of the former. The accuracy of our derived analytical results has been verified by extensive Monte-Carlo simulations. Our results show that increasing the number of RIS elements is always beneficial, but with diminishing returns when the number of RIS elements is sufficiently large. Furthermore, the effect of the number of AP antennas on system performance is more pronounced under a small number of RIS elements, while the spatial correlation of RIS elements imposes a more severe negative impact on the system performance than that of the AP antennas.

This paper proposes an integrated sensing, jamming, and communications (ISJC) framework for securing unmanned aerial vehicle (UAV)-enabled wireless networks. The proposed framework advocates the dual use of artificial noise transmitted by an information UAV for simultaneous jamming and sensing of an eavesdropping UAV. Based on the information sensed in the previous time slot, an optimization problem for online resource allocation design is formulated to maximize the number of securely served users in the current time slot, while taking into account a tracking performance constraint and quality-of-service (QoS) requirements regarding the leakage information rate to the eavesdropper and the downlink data rate to the legitimate users. A channel correlation-based algorithm is proposed to obtain a suboptimal solution for the design problem. Simulation results demonstrate the security benefits of integrating sensing into UAV communication systems.

The implementation of integrated sensing and communication (ISAC) highly depends on the effective beamforming design exploiting accurate instantaneous channel state information (ICSI). However, channel tracking in ISAC requires large amount of training overhead and prohibitively large computational complexity. To address this problem, in this paper, we focus on ISAC-assisted vehicular networks and exploit a deep learning approach to implicitly learn the features of historical channels and directly predict the beamforming matrix for the next time slot to maximize the average achievable sum-rate of system, thus bypassing the need of explicit channel tracking for reducing the system signaling overhead. To this end, a general sum-rate maximization problem with Cramer-Rao lower bounds-based sensing constraints is first formulated for the considered ISAC system. Then, a historical channels-based convolutional long short-term memory network is designed for predictive beamforming that can exploit the spatial and temporal dependencies of communication channels to further improve the learning performance. Finally, simulation results show that the proposed method can satisfy the requirement of sensing performance, while its achievable sum-rate can approach the upper bound obtained by a genie-aided scheme with perfect ICSI available.

This paper investigates multiuser multi-input single-output downlink symbiotic radio communication systems assisted by an intelligent reflecting surface (IRS). Different from existing methods ideally assuming the secondary user (SU) can jointly decode information symbols from both the access point (AP) and the IRS via multiuser detection, we consider a more practical SU that only non-coherent detection is available. To characterize the non-coherent decoding performance, a practical upper bound of the average symbol error rate (SER) is derived. Subsequently, we jointly optimize the beamformer at the AP and the phase shifts at the IRS to maximize the average sum-rate of the primary system taking into account the maximum tolerable SER constraint for the SU. To circumvent the couplings of variables, we exploit the Schur complement that facilitates the design of a suboptimal beamforming algorithm based on successive convex approximation. Our simulation results show that compared with various benchmark algorithms, the proposed scheme significantly improves the average sum-rate of the primary system, while guaranteeing the decoding performance of the secondary system.

This paper investigates resource allocation design for intelligent reflecting surface (IRS)-aided joint processing coordinated multipoint (JP-CoMP) downlink cellular networks with underlaying device-to-device (D2D) communications. In particular, the IRS is employed to establish favorable communication channel conditions and to mitigate the malignant interference caused by D2D devices. We aim to maximize the total weighted system sum-rate by jointly designing the cellular user (CU) association, the active beamforming at the base stations (BSs), the passive beamforming at the IRS, and the transmit power of each D2D transmitter. The resource allocation design is formulated as a non-convex optimization problem while taking into account the quality of service requirement of CUs and the power allocations for both CUs and D2D pairs. We propose a computationally efficient suboptimal iterative algorithm, which is guaranteed to converge to a Karush-Kuhn-Tucker (KKT) solution of the design problem. Simulation results demonstrate that the proposed scheme can significantly improve the system sum-rate over various baseline schemes adopting existing solutions. Also, our results confirm the superiority of introducing IRS for managing interference in wireless communication systems.

In this paper, we investigate a wireless-powered covert communication (WP-CC) system, where a full-duplex (FD) receiver transmits artificial noise (AN) to simultaneously charge an energy-constrained transmitter and to confuse a warden’s detection on the transmitter’s communication activity. A probabilistic accumulate-then-transmit (ATT) protocol, where the transmitter sends its information with a prior probability p conditioned on the available energy being sufficient, is proposed to maximize the communication covertness subject to a requirement on the communication quality. In order to facilitate the optimal design of the prior probability p and the information transmit power in the considered WP-CC system, we also derive the warden’s minimum detection error probability and characterize the effective covert rate from the transmitter to the receiver to quantify the communication covertness and quality, respectively. Our examination shows that the proposed probabilistic ATT protocol can achieve higher communication covertness than the traditional ATT protocol with p = 1.

Multi-hop transmissions are desirable in realizing large-scale long-distance covert wireless communications, since a single-hop transmission cannot fully satisfy the covertness requirement even with high transmit power. Against this background, this work compares amplify-and-forward (AF) and decode-and-forward (DF) relaying strategies by examining their achievable effective throughput taking into the covertness quality-of-service. To this end, we first present a framework of maximizing the effective throughput with the assumption that each relay adopts equal transmit power to seek mathematical tractability. With the number of relays and each relay’s transmit power optimized, our results reveal that DF relaying outperforms AF relaying in the considered multi-hop covert communications, in terms of achieving a higher effective throughput with a smaller optimal number of relays. This is mainly due to that regardless of the same detection performance at the warden Willie for AF and DF relaying under the same condition, AF relays amplify both information and noise signals, while DF relays only forward the information signals. In addition, our examinations show that DF relaying with independent codewords achieves a higher effective throughput with a fewer number of relays relative to the DF relaying with a single-codeword. Furthermore, we find that the optimal number of relays increases as the desired covert communication distance increases or the covertness constraint becomes stringent.

Security and energy efficiency have become crucial features in the modern-era wireless communication. In this paper, we consider an energy-efficient design for intelligent reflecting surface (IRS)-assisted multiple-input multiple-output multiple-eavesdropper (MIMOME) wiretap channels (WTC). Our objective is to jointly optimize the transmit covariance matrix and the IRS phase-shifts to maximize the secrecy energy efficiency (SEE) of the considered system subject to a secrecy rate constraint at the legitimate receiver. To tackle this challenging non-convex problem in which the design variables are coupled in the objective and the constraint, we propose a penalty dual decomposition based alternating gradient projection (PDDAPG) method to obtain an efficient solution. We also show that the computational complexity of the proposed algorithm grows only linearly with the number of reflecting elements at the IRS, as well as with the number of antennas at transmitter/receivers’ nodes. Our results confirm that using an IRS is helpful to improve the SEE of MIMOME WTC compared to its no-IRS counterpart only when the power consumption at IRS is small. In particular, and a large-sized IRS is not always beneficial for the SEE of a MIMOME WTC.

The initiation of fifth-generation (5G) wireless networks has been one of the latest innovations that the world has seen in recent years, which has also formed the foundation for the upcoming sixth-generation (6G) and beyond networks. The demand for high reliability with minimal delay of Internet of Things (IoT) applications has led to transmissions of short packets, which give rise to the proposal of ultra reliable and low-latency communications (URLLC). At a glance, the simultaneous demand for both reliability and minimal latency appears to be contradicting. However, by considering the impact of finite blocklength (FBL) regimes, the formulation of URLLC can be theoretically established under a wide range of practical scenarios such as severe fading, imperfect channel state information (iCSI), line-of-sight (LoS) fading and channel correlation. It has been noted that LoS, channel correlation and iCSI conditions have received interests among the community and their formulation illustrates practicality of theoretical signal processing research. Diversity has been deployed to combat fading, strengthen reliability and simultaneously reduce latency. Under specific environment, significant research has been developed and formulated to further the application of URLLC. The deployment of short packets however challenges Shannon's classical theory and opens a new research direction, which has attracted wide attention and research efforts around the world. Further, URLLC fundamentals can also be applied to intelligent reflective surface techniques, which clearly shows their applicability and usefulness. Myriads of work have been published and much more are coming to showcase the practicality of URLLC techniques and to explore its immense potential. This Special Issue (SI) has been formed to reflect on the latest developments on URLLC amid the fast-paced initiations and ideas. Through rigorous peer review processes, the selected papers have been chosen based on their high standard and diverse topics related to URLLC. The first paper by Q. Huang et al. examines the impact of cyclic redundancy check and rateless characteristic of cascading analogue fountain code under URLLC platform. The advanced inclusion of URLLC to 5G and 6G is also addressed and outlined, which makes this work timely. Cross verification and comparisons have been performed to show the effectiveness of their proposed method. Aiming to minimise end-to-end latency, the next paper of the SI treats the application of unmanned aerial vehicles (UAVs) under an URLLC platform using a Digital Twin (DT) approach. Edge computing is deployed to ensure that latency can be reduced to its minimum, and then optimisation is extra performed so that satisfactory results can be obtained. This work brings direct benefit to industry automation by exploiting K-mean algorithms for optimisation. Task offloading has been specifically considered to test the efficiency of the propped DT framework under URLLC platform. The third paper is dedicated to vehicular communications and the effectiveness of energy harvesting roadside units. This paper begins by proposing an attention-based spatial-temporal graph convolutional network to estimate vehicular communication load. A simulated data set was created to validate the proposed work, which potentially has commercialisation by lowering the cost of EHU-RSs. The fourth paper addresses one of the most fundamental problems of URLLC, which is joint power and blocklength optimisation for reflective intelligent surfaces (RIS) under FBL regimes using time division multiple access techniques. The effectiveness of the RIS under the FBL regimes has been clearly shown. Summary: This Special Issue has given a glimpse of URLLC capability from theoretical point of view to its wide applications, which involve UAVs and other smart devices. The prospect of URLLC is evolving into its advanced version in future wireless networks. The functionality of URLLC and other techniques is to ultimately serve the end user to strengthen convenience, practicality and technology advancement. From this point of view, the current URLLC framework has sufficiently ticked certain boxes. The Special Issue Editors would like to thank the Editor in Chief Professor James Hopgood, the Managing Editor Emily Summerbell and the editorial office for their constant support without which this Special Issue would not be possible. Research data are not shared. Khoa N. Le received his Ph.D. in October 2002 from Monash University, Melbourne, Australia. From April 2003 to June 2009, he was a Lecturer at Griffith University, Gold Coast campus, Griffith School of Engineering. From January to July 2008, he was a visiting professor at Intelligence Signal Processing Laboratory, Korea University, Seoul, Korea. From January 2009 to February 2009, he was a visiting professor at the Wireless Communication Centre, University Technology Malaysia, Johor Bahru, Malaysia. He is currently Associate Professor and Director of Academic Programme, Undergraduate, Electrical Engineering, School of Engineering, Design and Built Environment, Kingswood, Western Sydney University. His research interests are in wireless communications theory with applications to physical layer security and URLLC. Dr. Le has been Editor for IEEE Transactions on Vehicular Technology, IEEE Wireless Communication Magazine, and he is currently Deputy Editor In Chief of IET Signal Processing. He was listed in World's Top 2% scientists for 2021 and Bayu Chair Professor, Chongqing University of Science and Technology, Chongqing, China, 2020–2022. Derrick Wing Kwan Ng (S′06-M′12-SM′17-F′21) received a bachelor's degree with first-class honours and a Master of Philosophy (M.Phil.) degree in electronic engineering from the Hong Kong University of Science and Technology (HKUST) in 2006 and 2008, respectively. He received his Ph.D. degree from the University of British Columbia (UBC) in Nov. 2012. He was a senior postdoctoral fellow at the Institute for Digital Communications, Friedrich-Alexander-University Erlangen-N\"urnberg (FAU), Germany. He is now working as a Scientia Associate Professor at the University of New South Wales, Sydney, Australia. His research interests include global optimization, physical layer security, IRS-assisted communication, UAV-assisted communication, wireless information and power transfer, and green (energy-efficient) wireless communications. Ng has been listed as a Highly Cited Researcher by Clarivate Analytics (Web of Science) since 2018. He received the Australian Research Council (ARC) Discovery Early Career Researcher Award 2017, the IEEE Communications Society Stephen O. Rice Prize 2022, the Best Paper Awards at the WCSP 2020, 2021, IEEE TCGCC Best Journal Paper Award 2018, INISCOM 2018, IEEE International Conference on Communications (ICC) 2018, 2021, IEEE International Conference on Computing, Networking and Communications (ICNC) 2016, IEEE Wireless Communications and Networking Conference (WCNC) 2012, the IEEE Global Telecommunication Conference (Globecom) 2011, 2021 and the IEEE Third International Conference on Communications and Networking in China 2008. He served as an editorial assistant to the Editor-in-Chief of the IEEE Transactions on Communications from Jan. 2012 to Dec. 2019. He is now serving as an editor for the IEEE Transactions on Communications, the IEEE Transactions on Wireless Communications, and an Associate Editor-in-Chief for the IEEE Open Journal of the Communications Society. Zhiguo Ding (S′03-M′05-F′20) received his B.Eng in Electrical Engineering from the Beijing University of Posts and Telecommunications in 2000, and the Ph.D degree in Electrical Engineering from Imperial College London in 2005. From Jul. 2005 to Apr. 2018, he was working in Queen's University Belfast, Imperial College, Newcastle University and Lancaster University. Since Apr. 2018, he has been with the University of Manchester as a Professor in Communications. From Oct. 2012 to Sept. 2022, he has also been an academic visitor in Princeton University. Ding’ research interests are B5G networks, machine learning, cooperative and energy harvesting networks and statistical signal processing. He is serving as an Area Editor for the IEEE Open Journal of the Communications Society, an Editor for IEEE Transactions on Vehicular Technology, and IEEE Communications Surveys & Tutorials, and was an Editor for IEEE Wireless Communication Letters, IEEE Transactions on Communications, IEEE Communication Letters from 2013 to 2016. He recently received the EU Marie Curie Fellowship 2012–2014, the Top IEEE TVT Editor 2017, IEEE Heinrich Hertz Award 2018, IEEE Jack Neubauer Memorial Award 2018, IEEE Best Signal Processing Letter Award 2018, Friedrich Wilhelm Bessel Research Award 2020, and Best Paper Award at IEEE ICCC-2021. He is a Fellow of the IEEE, a Distinguished Lecturer of IEEE ComSoc, and a Web of Science Highly Cited Researcher in two categories 2020.

Device-to-device (D2D) communication is regarded as a promising technology to support spectral-efficient Internet of Things (IoT) in beyond fifth-generation (5G) and sixth-generation (6G) networks. This article investigates the spectrum access problem for D2D-assisted cellular networks based on deep reinforcement learning (DRL), which can be applied to both the uplink and downlink scenarios. Specifically, we consider a time-slotted cellular network, where D2D nodes share the cellular spectrum resources (CUEs) with cellular users in a time-splitting manner. Besides, D2D nodes could reuse time slots preoccupied by CUEs according to a location-based spectrum access (LSA) strategy on the premise of cellular communication quality. The key challenge lies in that D2D nodes have no information on the LSA strategy and the access principle of CUEs. Thus, we design a DRL-based spectrum access scheme such that the D2D nodes can autonomously acquire an optimal strategy for efficient spectrum access without any prior knowledge to achieve a specific objective such as maximizing the normalized sum throughput. Moreover, we adopt a generalized double deep$Q$-network (DDQN) algorithm and extend the objective function to explore the resource allocation fairness for D2D nodes. The proposed scheme is evaluated under various conditions and our simulation results show that it can achieve the near-optimal throughput performance with different objectives compared to the benchmark, which is the theoretical throughput upper bound derived from a genius-aided scheme with complete system knowledge available.

In this work, we aim to maximize the timeliness of data collection subject to a covertness constraint in unmanned aerial vehicle (UAV)-aided Internet of Things (IoT) networks, where a UAV periodically conducts wireless power transfer (WPT) to charge an energy-constrained IoT device and then the IoT device opportunistically sends its collected data to the UAV. To this end, we first derive a lower bound on the covertness constraint and an analytical expression for Age of Information (AoI) to characterize timeliness. Then, we jointly optimize the UAV’s transmit power for WPT, the WPT duration, and the data transmission duration by considering two practical scenarios. For the fixed total duration scenario, our analytical optimal solutions indicate that the total harvested energy at the IoT device is independent of the covertness constraint, although both the UAV’s transmit power and the WPT duration are significantly affected by the covertness constraint. With the optimized total duration scenario, we prove that the UAV’s optimal transmit power is always attained at its maximum value regardless of the existence of the covertness constraint, but the WPT and data transmission durations are sensitive to the required covertness. Overall, there exists a nontrivial tradeoff between the timeliness and covertness for data collection in the considered system, which is determined by the WPT design. Furthermore, our numerical results show that the optimal prior probability of the IoT device’s opportunistic transmission is generally not 0.5 for the fixed total duration, but it is indeed 0.5 for the optimized total duration.

Radio-frequency (RF) energy harvesting (EH) in wireless relaying networks has attracted considerable recent interest, especially for supplying energy to relay nodes in the Internet of Things (IoT) systems to assist the information exchange between a source and a destination. Moreover, limited hardware, computational resources, and energy availability of IoT devices have raised various security challenges. To this end, physical-layer security (PLS) has been proposed as an effective alternative to cryptographic methods for providing information security. In this study, we propose a PLS approach for simultaneous wireless information and power transfer (SWIPT)-based half-duplex (HD) amplify-and-forward (AF) relaying systems in the presence of an eavesdropper. Furthermore, we take into account both static power splitting relaying (SPSR) and dynamic power splitting relaying (DPSR) to thoroughly investigate the benefits of each one. To further enhance secure communication, we consider multiple friendly jammers to help prevent wiretapping attacks from the eavesdropper. More specifically, we provide a reliability and security analysis by deriving closed-form expressions of outage probability (OP) and intercept probability (IP), respectively, for both the SPSR and DPSR schemes. Then, simulations are also performed to validate our analysis and the effectiveness of the proposed schemes. Specifically, numerical results illustrate the nontrivial tradeoff between reliability and security of the proposed system. In addition, we conclude from the simulation results that the proposed DPSR scheme outperforms the SPSR-based scheme in terms of OP and IP under the influences of different parameters on system performance.

In this article, we investigate an unsourced random access scheme for massive machine-type communications (mMTC) in the sixth-generation (6G) wireless networks with sporadic data traffic. First, we establish a general framework for massive unsourced random access based on a two-layer signal coding, i.e., an outer code and an inner code. In particular, considering Rician fading in the scenario of mMTC, we design a novel codeword activity detection algorithm for the inner code of unsourced random access based on the distribution of received signals by exploiting the maximum-likelihood (ML) method. Then, we analyze the performance of the proposed codeword activity detection algorithm exploiting Fisher Information Matrix, which facilitates the derivative of the approximated distribution of the estimation error of the codeword activity vector when the number of base station (BS) antennas is sufficiently large. Furthermore, for the outer code, we propose an optimization algorithm to allocate the lengths of message bits and parity check bits, so as to strike a balance between the error probability and the complexity required for outer decoding. Finally, extensive simulation results validate the effectiveness of the proposed detection algorithm and the optimized length allocation scheme compared with an existing detection algorithm and a fixed-length allocation scheme.

In this article, we investigate an unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) system in a sophisticated 3-D environment, where the UAV’s trajectory is optimized to efficiently collect data from multiple IoT ground nodes. Unlike existing approaches focusing only on a simplified 2-D scenario and the availability of perfect channel state information (CSI), this article considers a practical 3-D urban environment with imperfect CSI, where the UAV’s trajectory is designed to minimize data collection completion time subject to practical throughput and flight movement constraints. Specifically, inspired by the state-of-the-art deep reinforcement learning approaches, we leverage the twin-delayed deep deterministic policy gradient (TD3) to design the UAV’s trajectory and we present a TD3-based trajectory design for completion time minimization (TD3-TDCTM) algorithm. In particular, we set an additional information, i.e., the merged pheromone, to represent the state information of the UAV and environment as a reference of reward which facilitates the algorithm design. By taking the service statuses of the IoT nodes, the UAV’s position, and the merged pheromone as input, the proposed algorithm can continuously and adaptively learn how to adjust the UAV’s movement strategy. By interacting with the external environment in the corresponding Markov decision process, the proposed algorithm can achieve a near-optimal navigation strategy. Our simulation results show the superiority of the proposed TD3-TDCTM algorithm over three conventional nonlearning-based baseline methods.

This article investigates the performance of nonorthogonal multiple access (NOMA)-based cell-free massive multiple-input–multiple-output (mMIMO) for the Internet of Things (IoT) considering spatially correlated Rician fading channels. The exact closed form of downlink spectral efficiency (SE) and energy efficiency expressions is derived with three estimators and the maximum ratio transmission by taking the impacts of imperfect successive interference cancellation and pilot contamination into account. Subsequently, the performance of a local-MMSE precoder with the three aforementioned estimators is analyzed. Then, a large-scale fading-based user pairing scheme is proposed to further analyze the system SE. Besides, we formulate the optimum power control design as a max–min problem and a computational efficient suboptimal algorithm is proposed based on the successive convex approximation. Furthermore, our results reveal that the magnitude of the spatial correlation negligibly effects the SE in spatially correlated Rician fading channels. Then, numerical results confirm the positive effect of the proposed power control scheme. Also, our results further illustrate that NOMA-based cell-free mMIMO for IoT provides significant performance gain compared with its counterpart deploying conventional orthogonal multiple-access schemes.

This paper investigates the integrated sensing and communication (ISAC) in vehicle-to-infrastructure (V2I) networks. To realize ISAC, an effective beamforming design is essential which however, highly depends on the availability of accurate channel tracking requiring large training overhead and computational complexity. Motivated by this, we adopt a deep learning (DL) approach to implicitly learn the features of historical channels and directly predict the beamforming matrix to be adopted for the next time slot to maximize the average achievable sum-rate of an ISAC system. The proposed method can bypass the need of explicit channel tracking process and reduce the signaling overhead significantly. To this end, a general sum-rate maximization problem with Cramer-Rao lower bounds-based sensing constraints is first formulated for the considered ISAC system taking into account the multiple access interference. Then, by exploiting the penalty method, a versatile unsupervised DL-based predictive beamforming design framework is developed to address the formulated design problem. As a realization of the developed framework, a historical channels-based convolutional long short-term memory (LSTM) network (HCL-Net) is devised for predictive beamforming in the ISAC-based V2I network. Specifically, the convolution and LSTM modules are successively adopted in the proposed HCL-Net to exploit the spatial and temporal dependencies of communication channels to further improve the learning performance. Finally, simulation results show that the proposed predictive method not only guarantees the required sensing performance, but also achieves a satisfactory sum-rate that can approach the upper bound obtained by the genie-aided scheme with the perfect instantaneous channel state information available.

The core requirement of massive Machine-Type Communication (mMTC) is to support reliable and fast access for an enormous number of machine-type devices (MTDs). In many practical applications, the base station (BS) only concerns the list of received messages instead of the source information, introducing the emerging concept of unsourced random access (URA). Although some massive multiple-input multiple-output (MIMO) URA schemes have been proposed recently, the unique propagation properties of millimeter-wave (mmWave) massive MIMO systems are not fully exploited in conventional URA schemes. In grant-free random access, the BS cannot perform receive beamforming independently as the identities of active users are unknown to the BS. Therefore, only the intrinsic beam division property can be exploited to improve the decoding performance. In this paper, a URA scheme based on beam-space tree decoding is proposed for mmWave massive MIMO system. Specifically, two beam-space tree decoders are designed based on hard decision and soft decision, respectively, to utilize the beam division property. They both leverage the beam division property to assist in discriminating the sub-blocks transmitted from different users. Besides, the first decoder can reduce the searching space, enjoying a low complexity. The second decoder exploits the advantage of list decoding to recover the miss-detected packets. Simulation results verify the superiority of the proposed URA schemes compared to the conventional URA schemes in terms of error probability.

Accurate channel prediction is vital to address the channel aging issue in mobile communications with fast time-varying channels. Existing channel prediction schemes are generally based on the sequential signal processing, i.e., the channel in the next frame can only be sequentially predicted. Thus, the accuracy of channel prediction rapidly degrades with the evolution of frame due to the error propagation problem in the sequential operation. To overcome this challenging problem, we propose a transformer-based parallel channel prediction scheme to predict future channels in parallel. Specifically, we first formulate the channel prediction problem as a parallel channel mapping problem, which predicts the channels in next several frames in parallel. Then, inspired by the recently proposed parallel vector mapping model named transformer, a transformer-based parallel channel prediction scheme is proposed to solve this formulated problem. Relying on the attention mechanism in machine learning, the transformer-based scheme naturally enables parallel signal processing to avoid the error propagation problem. The transformer can also adaptively assign more weights and resources to the more relevant historical channels to facilitate accurate prediction for future channels. Moreover, we propose a pilot-to-precoder (P2P) prediction scheme that incorporates the transformer-based parallel channel prediction as well as pilot-based channel estimation and precoding. In this way, the dedicated channel estimation and precoding can be avoided to reduce the signal processing complexity. Finally, simulation results verify that the proposed schemes are able to achieve a negligible sum-rate performance loss for practical 5G systems in mobile scenarios.

Faster-than-Nyquist (FTN) signaling aided non-orthogonal multiple access (NOMA) is conceived and its achievable rate is quantified in the presence ofrandomlink delays of the different users. We reveal that exploiting the link delays may potentially lead to a signal-to-interference-plus-noise ratio (SINR) gain, while transmitting the data symbols at FTN rates has the potential of increasing the degree-of-freedom (DoF). We then unveil the fundamental trade-off between the SINR and DoF. In particular, at a sufficiently high symbol rate, the SINR gain vanishes while the DoF gain achieves its maximum, where the achievable rate is almost$(1+\beta)$times higher than that of the conventional synchronous NOMA transmission in the high signal-to-noise ratio (SNR) regime, with$\beta $being the roll-off factor of the signaling pulse. Our simulation results verify our analysis and demonstrate considerable rate improvements over the conventional power-domain NOMA scheme.

In this paper, we propose a novel integrated sensing and communication (ISAC) transmission framework based on the spatially spread orthogonal time frequency space (SS-OTFS) modulation by considering the fact that communication channel strengths cannot be directly obtained from radar sensing. We first propose the concept of SS-OTFS modulation, where the key novelty is the angular domain discretization enabled by the spatial spreading/de-spreading. This discretization gives rise to simple and insightful effective models for both radar sensing and communication, which results in simplified designs for the related estimation and detection problems. In particular, we design simple beam tracking, angle estimation, and power allocation schemes for radar sensing, by utilizing the special structure of the effective radar sensing matrix. Meanwhile, we provide a detailed analysis on the pair-wise error probability (PEP) for communication, which unveils the key conditions for both precoding and power allocation designs for communication. Based on those conditions, we design a symbol-wise precoding scheme for communication based only on the delay, Doppler, and angle estimates from radar sensing, without thea prioriknowledge of the communication channel fading coefficients, and also propose a suitable power allocation. Furthermore, we notice that radar sensing and communication requires different power allocations. Therefore, we discuss the performances of both the radar sensing and communication with different power allocations and show that the power allocation should be designed leaning towards radar sensing in practical scenarios. The effectiveness of the proposed ISAC transmission framework is verified by our numerical results, which also agree with our analysis and discussions.

International audience

Over the last decade, simultaneous wireless information and power transfer (SWIPT) has become a practical and promising solution for connecting and recharging battery-limited devices due to significant advances in low-power electronics technology and wireless communications techniques. To realize the promised potentials, advanced resource allocation design plays a decisive role in revealing, understanding, and exploiting the intrinsic rate–energy tradeoff capitalizing on the dual use of radio frequency (RF) signals for wireless charging and communication. In this article, we provide a comprehensive tutorial overview of SWIPT from the perspective of resource allocation design. The fundamental concepts, system architectures, and RF energy harvesting (EH) models are introduced. In particular, three commonly adopted EH models, namely, the linear EH model, the nonlinear saturation EH model, and the nonlinear circuit-based EH model, are characterized and discussed. Then, for a typical wireless system setup, we establish a generalized resource allocation design framework that subsumes conventional resource allocation design problems as special cases. Subsequently, we elaborate on relevant tools from optimization theory and exploit them for solving representative resource allocation design problems for SWIPT systems with and without perfect channel state information (CSI) available at the transmitter, respectively. The associated technical challenges and insights are also highlighted. Furthermore, we discuss several promising and exciting future research directions for resource allocation design for SWIPT systems intertwined with cutting-edge communication technologies, such as intelligent reflecting surfaces, unmanned aerial vehicles, mobile edge computing, federated learning, and machine learning.

The roll-out of various emerging wireless services has triggered the need for the sixth-generation (6G) wireless networks to provide functions of target sensing, intelligent computing and information communication over the same radio spectrum. In this paper, we provide a unified framework integrating sensing, computing, and communication to optimize limited system resource for 6G wireless networks. In particular, two typical joint beamforming design algorithms are derived based on multi-objective optimization problems (MOOP) with the goals of the weighted overall performance maximization and the total transmit power minimization, respectively. Extensive simulation results validate the effectiveness of the proposed algorithms. Moreover, the impacts of key system parameters are revealed to provide useful insights for the design of integrated sensing, computing, and communication (ISCC).

The integration of aerial platforms to provide ubiquitous coverage and connectivity for densely deployed terrestrial networks is expected to be a reality in the emerging sixth-generation networks. Energy-effificient and secure transmission designs are two important components for integrated terrestrial-aerial networks (ITAN). Inlight of the potential of reconfigurable intelligent surface (RIS) for significantly reducing the system power consumption and boosting information security, this paper proposes a multi-layer RIS-assisted secure ITAN architecture to defend against simultaneous jamming and eavesdropping attacks, and investigates energy-efficient hybrid beamforming for it. Specifically, with the availability of imperfect angular channel state information (CSI), we propose a block coordinate descent (BCD) framework for the joint optimization of the user’s received decoder, the terrestrial and aerial digital precoder, and the multi-layer RIS analog precoder to maximize the system energy efficiency (EE) performance. For the design of the received decoder, a heuristic beamforming scheme is proposed to convert the worst-case design problem into a min-max one and facilitate the developing a closed-form solution. For the design of the digital precoder, we propose an iterative sequential convex approximation approach via capitalizing the auxiliary variables and first-order Taylor series expansion. Finally, a monotonic vertex-update algorithm with a penalty convex-concave procedure (P-CCP) is proposed to obtain the analog precoder with satisfactory performance. Numerical results show the superiority and effectiveness of the proposed optimization framework and architecture over various benchmark schemes.

Edge federated learning (FL) is an emerging paradigm that trains a global parametric model from distributed datasets based on wireless communications. This paper proposes a unit-modulus over-the-air computation (UMAirComp) framework to facilitate efficient edge federated learning, which simultaneously uploads local model parameters and updates global model parameters via analog beamforming. The proposed framework avoids sophisticated baseband signal processing, leading to low communication delays and implementation costs. Training loss bounds of UMAirComp FL systems are derived and two low-complexity large-scale optimization algorithms, termed penalty alternating minimization (PAM) and accelerated gradient projection (AGP), are proposed to minimize the nonconvex nonsmooth loss bound. Simulation results show that the proposed UMAirComp framework with PAM algorithm achieves a smaller mean square error of model parameters’ estimation, training loss, and test error compared with other benchmark schemes. Moreover, the proposed UMAirComp framework with AGP algorithm achieves satisfactory performance while reduces the computational complexity by orders of magnitude compared with existing optimization algorithms. Finally, we demonstrate the implementation of UMAirComp in a vehicle-to-everything autonomous driving simulation platform. It is found that autonomous driving tasks are more sensitive to model parameter errors than other tasks since the neural networks for autonomous driving contain sparser model parameters.

In this paper, we study the optimal resource allocation algorithm design for large intelligent reflecting surface (IRS)-assisted simultaneous wireless information and power transfer (SWIPT) systems. To facilitate efficient system design for large IRSs, instead of jointly optimizing all the IRS elements, we partition the IRS into several tiles and employ a scalable optimization framework comprising an offline design stage and an online optimization stage. In the offline stage, the IRS elements of each tile are jointly designed to support a set of different phase shift configurations, referred to as transmission modes, while the best transmission mode is selected from the set for each tile in the online stage. Given a transmission mode set, we aim to minimize the total base station (BS) transmit power by jointly optimizing the beamforming and the transmission mode selection policy taking into account the quality-of-service requirements of information decoding and non-linear energy harvesting receivers, respectively. Although the resource allocation algorithm design is formulated as a non-convex combinatorial optimization problem, we solve it optimally by applying the branch-and-bound (BnB) approach which entails a high computational complexity. To strike a balance between optimality and computational complexity, we also develop an efficient suboptimal algorithm capitalizing on the penalty method and successive convex approximation. Our simulation results show that the proposed designs enable considerable power savings compared to several baseline schemes. Moreover, our results reveal that by properly adjusting the numbers of tiles and transmission modes, the proposed scalable optimization framework indeed facilitates online design for large IRSs. Besides, our results confirm that the advocated physics-based model and scalable optimization framework enable a flexible trade-off between performance and complexity, which is vital for realizing the performance gains promised by large IRS-assisted communication systems in practice.

In this paper, we investigate the robust resource allocation design for secure communication in an integrated sensing and communication (ISAC) system. A multi-antenna dual-functional radar-communication (DFRC) base station (BS) serves multiple single-antenna legitimate users and senses for targets simultaneously, where already identified targets are treated as potential single-antenna eavesdroppers. The DFRC BS scans a sector with a sequence of dedicated beams, and the ISAC system takes a snapshot of the environment during the transmission of each beam. Based on the sensing information, the DFRC BS can acquire the channel state information (CSI) of the potential eavesdroppers. Different from existing works that focused on the resource allocation design for a single snapshot, in this paper, we propose a novel optimization framework that jointly optimizes the communication and sensing resources over a sequence of snapshots with adjustable durations. Besides, artificial noise (AN) is exploited by the BS for joint sensing and physical layer security provisioning. To this end, we jointly optimize the duration of each snapshot, the beamforming vector, and the covariance matrix of the AN for maximization of the system sum secrecy rate over a sequence of snapshots while guaranteeing a minimum required average achievable rate and a maximum information leakage constraint for each legitimate user. The resource allocation algorithm design is formulated as a non-convex optimization problem, where we account for the imperfect CSI of both the legitimate users and the potential eavesdroppers. To make the problem tractable, we derive a bound for the uncertainty region of the potential eavesdroppers’ small-scale fading based on a safe approximation, which facilitates the development of a block coordinate descent-based iterative algorithm for obtaining an efficient suboptimal solution. Simulation results illustrate that the proposed scheme can significantly enhance the physical layer security of ISAC systems compared to three baseline schemes. Moreover, compared to the conventional multi-stage approach for ISAC system design, the proposed approach based on variable-length snapshots not only facilitates a highly-directional offline sensing beam design but also allows us to flexibly prioritize communication or sensing depending on the application scenario.

Massive multiple-input multiple-output (MIMO) integrated with millimeter-wave (mmWave) can provide unprecedented performance improvement for realizing future wireless communications. However, acquiring accurate channel state information in wideband mmWave massive MIMO systems with hybrid transceiver architectures is even challenging, especially in high-mobility scenarios with severe Doppler effects. In this paper, we propose a tensor decomposition-based method to estimate the time-varying and frequency-selective (TVFS) mmWave MIMO channels. Specifically, by exploiting the sparse scattering nature of TVFS channels, we model the frequency-domain received signal as a third-order tensor that admits a canonical polyadic (CP) decomposition format. Then, we analyze the uniqueness condition of the proposed CP decomposition-based channel estimation problem and propose a novel estimator to acquire TVFS channel parameters including angle of departure/arrival (AoD/AoA), time delay, path gain, and the Doppler shift. To address the sophisticated coupling among unknown parameters, we further propose a joint AoD and Doppler shift estimation (JADE) algorithm that provides reliable initial and iteratively refined estimates. The derived analysis and simulation results verify that the proposed JADE algorithm achieves higher estimation accuracy and guarantees the superiority of the proposed TVFS channel estimator over existing schemes.

The physical layer security (PLS) of millimeter wave (mmWave) communication systems is investigated, where the secure source-to-destination communication is assisted by an untrusted relay selected from a group of them and there are also several passive eavesdroppers (Eves) in the network. In the considered system model, while the distributions of the untrusted relays and Eves follow a homogeneous Poisson Point Process (PPP). To maximize the instantaneous secrecy rate, a novel joint relay selection and power allocation (JRP) method is developed where the destination and source aim for jamming the reception of both the untrusted relays and passive Eves. New expressions of the optimal power allocation (OPA) are derived for both non-colluding Eves (NCE) and colluding Eves (CE). Subsequently, by considering the impact of potential blockages, new closed-form equations are derived for analyzing the system’s ergodic secrecy rate (ESR) and secrecy outage probability (SOP) for transmission over fading mmWave channels. Finally, numerical examples are provided for demonstrating the superiority of our proposed JRP method over the relevant benchmarks found in the literature. Interestingly, the ESR increases with the density of untrusted relays for both the NCE and CE scenarios, which is a benefit of the improved probability of selecting a relay with a stronger second-hop channel. Furthermore, in the low transmit power regime, employing relatively low mmWave frequencies achieves better ESR, while in the high transmit power regime, high mmWave frequencies provide higher ESR.

In this paper, we propose a novel joint resource allocation and cooperative caching scheme for power-domain non-orthogonal multiple access (PD-NOMA)-based heterogeneous networks (HetNets). In our scheme, the requested content is fetched directly from the edge if it is cached in the storage of one of the base stations (BSs), and otherwise is fetched via the backhaul. Our scheme consists of two phases: 1) Caching phase where the contents are saved in the storage of the BSs; and 2) Delivery phase where the requested contents are delivered to users. We formulate a novel optimization problem over radio resources and content placement variables. We aim to minimize the network cost subject to quality-of-service (QoS), caching, subcarrier assignment, and power allocation constraints. By exploiting advanced optimization methods, such as alternative search method (ASM), Hungarian algorithm, successive convex approximation (SCA), we obtain an efficient sub-optimal solution of the optimization problem. Numerical results illustrate that our ergodic caching policy via the proposed resource management algorithm can achieve a considerable reduction on the total cost on average compared to the most popular caching and random caching policy. Moreover, our cooperative NOMA scheme outperforms orthogonal multiple access (OMA) in terms of the delivery cost in general with an acceptable complexity increase.

In this paper, an intelligent reflecting surface (IRS) is introduced to assist an unmanned aerial vehicle (UAV) communication system based on non-orthogonal multiple access (NOMA) for serving multiple ground users. We aim to minimize the average total system energy consumption by jointly designing the resource allocation strategy, the three dimensional (3D) trajectory of the UAV, as well as the phase control at the IRS. The design is formulated as a non-convex optimization problem taking into account the maximum tolerable outage probability constraint and the individual minimum data rate requirement. To circumvent the intractability of the design problem due to the altitude-dependent Rician fading in UAV-to-user links, we adopt the deep neural network (DNN) approach to accurately approximate the corresponding effective channel gains, which facilitates the development of a low-complexity suboptimal iterative algorithm via dividing the formulated problem into two subproblems and address them alternatingly. Numerical results demonstrate that the proposed algorithm can converge to an effective solution within a small number of iterations and illustrate some interesting insights: (1) IRS enables a highly flexible UAV’s 3D trajectory design via recycling the dissipated radio signal for improving the achievable system data rate and reducing the flight power consumption of the UAV; (2) IRS provides a rich array gain through passive beamforming in the reflection link, which can substantially reduce the required communication power for guaranteeing the required quality-of-service (QoS); (3) Optimizing the altitude of UAV’s trajectory can effectively exploit the outage-guaranteed effective channel gain to save the total required communication power enabling power-efficient UAV communications; (4) NOMA communications offer higher degrees of freedom (DoF) than that of the conventional orthogonal multiple access (OMA) scheme to minimize the average power consumption via optimizing the UAV’s trajectory.

Channel estimation is one of the main tasks in realizing practical intelligent reflecting surface-assisted multi-user communication (IRS-MUC) systems. However, different from traditional communication systems, an IRS-MUC system generally involves a cascaded channel with a sophisticated statistical distribution. In this case, the optimal minimum mean square error (MMSE) estimator requires the calculation of a multidimensional integration which is intractable to be implemented in practice. To further improve the channel estimation performance, in this paper, we model the channel estimation as a denoising problem and adopt a deep residual learning (DReL) approach to implicitly learn the residual noise for recovering the channel coefficients from the noisy pilot-based observations. To this end, we first develop a versatile DReL-based channel estimation framework where a deep residual network (DRN)-based MMSE estimator is derived in terms of Bayesian philosophy. As a realization of the developed DReL framework, a convolutional neural network (CNN)-based DRN (CDRN) is then proposed for channel estimation in IRS-MUC systems, in which a CNN denoising block equipped with an element-wise subtraction structure is specifically designed to exploit both the spatial features of the noisy channel matrices and the additive nature of the noise simultaneously. In particular, an explicit expression of the proposed CDRN is derived and analyzed in terms of Bayesian estimation to characterize its properties theoretically. Finally, simulation results demonstrate that the performance of the proposed method approaches that of the optimal MMSE estimator requiring the availability of the prior probability density function of channel.

Massive machine-type communications (mMTC) are poised to provide ubiquitous connectivity for billions of Internet-of-Things (IoT) devices. However, the required low-latency massive access necessitates a paradigm shift in the design of random access schemes, which invokes a need of efficient joint activity and data detection (JADD) algorithms. By exploiting the feature of sporadic traffic in massive access, a beacon-aided slotted grant-free massive access solution is proposed. Specifically, we spread the uplink access signals in multiple subcarriers with pre-equalization processing and formulate the JADD as a multiple measurement vectors (MMV) compressive sensing problem. Moreover, to leverage the structured sparsity of uplink massive access signals among multiple time slots, we develop two computationally efficient detection algorithms, which are termed as orthogonal approximate message passing (OAMP)-MMV algorithm with simplified structure learning (SSL) and accurate structure learning (ASL). To achieve accurate detection, the expectation maximization algorithm is exploited for learning the sparsity ratio and the noise variance. To further improve the detection performance, channel coding is applied and successive interference cancellation (SIC)-based OAMP-MMV-SSL and OAMP-MMV-ASL algorithms are developed, where the likelihood ratio obtained in the soft-decision can be exploited for refining the activity identification. Finally, the state evolution of the proposed OAMP-MMV-SSL and OAMP-MMV-ASL algorithms is derived to predict the performance theoretically. Simulation results verify that the proposed solutions outperform various state-of-the-art baseline schemes, enabling low-latency random access and high-reliable massive IoT connectivity with overloading.

The massive machine-type communications (mMTC) paradigm based on media modulation in conjunction with massive multi-input multi-output base stations (BSs) is emerging as a viable solution to support the massive connectivity for the future Internet-of-Things, in which the inherent massive access at the BSs poses significant challenges for device activity and data detection (DADD). This paper considers the DADD problem for both uncoded and coded media modulation based mMTC with a slotted access frame structure, where the device activity remains unchanged within one frame. Specifically, due to the slotted access frame structure and the adopted media modulated symbols, the access signals exhibit adoubly structured sparsityin both the time domain and the modulation domain. Inspired by this, a doubly structured approximate message passing (DS-AMP) algorithm is proposed for reliable DADD in the uncoded case. Also, we derive the state evolution of the DS-AMP algorithm to theoretically characterize its performance. As for the coded case, we develop a bit-interleaved coded media modulation scheme and propose an iterative DS-AMP (IDS-AMP) algorithm based on successive inference cancellation (SIC), where the signal components associated with the detected active devices are successively subtracted to improve the data decoding performance. In addition, the channel estimation problem for media modulation based mMTC is discussed and an efficient data-aided channel state information (CSI) update strategy is developed to reduce the training overhead in block fading channels. Finally, simulation results and computational complexity analysis verify the superiority of the proposed DS-AMP algorithm over state-of-the-art algorithms in the uncoded case. Also, our results confirm that the proposed SIC-based IDS-AMP algorithm can enhance the data decoding performance in the coded case and verify the validity of the proposed data-aided CSI update strategy.

This paper investigates a reconfigurable intelligent surface (RIS)-aided unsourced random access (URA) scheme for the sixth-generation (6G) wireless networks with massive sporadic traffic devices. First of all, this paper proposes a novel joint active device separation (the message recovery of active device) and channel estimation architecture for the RIS-aided URA. Specifically, the RIS passive reflection is optimized before the successful device separation. Then, by associating the data sequences to multiple rank-one tensors and exploiting the angular sparsity of the RIS-BS channel, the detection problem is cast as a high-order coupled tensor decomposition problem without the need of exploiting pilot sequences. However, the inherent coupling among multiple sparse device-RIS channels, together with the unknown number of active devices make the detection problem at hand deviate from the widely-used coupled tensor decomposition format. To overcome this challenge, this paper judiciously devises a probabilistic model that captures both the element-wise sparsity from the angular channel model and the low-rank property due to the sporadic nature of URA. Then, based on such a probabilistic model, a iterative detection algorithm is developed under the framework of sparse variational inference, where each update iteration is obtained in a closed-form and the number of active devices can be automatically estimated for effectively avoiding the overfitting of noise. Extensive simulation results confirm the excellence of the proposed URA algorithm, especially for the case of a large number of reflecting elements for accommodating a significantly large number of devices.

In this paper, we consider the problem of full-duplex covert millimeter wave (mmWave) communications, where a mmWave transmitter (Alice) sends information signals to its intended receiver (Bob) covertly in the presence of a watchful warden (Willie). For covering the presence of Alice, Bob operates in the full-duplex mode and generates jamming signals with a time-varying power. We investigate the covert rate optimization for both the single data stream case and the multiple data streams case under the constraints of the detection error probability at Willie. Specifically, for the single data stream case, we analytically characterize the minimum detection error probability at Willie and establish a framework for optimizing the analog beamforming, transmit power, and analog jamming jointly. As for the case of multiple data streams, we derive a tractable lower bound of the minimum detection error probability at Willie and formulate a joint optimization of the hybrid precoder and analog jamming design problem for the maximization of the achievable covert rate. Although the joint design problem is nonconvex, we adopt the penalty decomposition technique to handle the effect of the coupling between the analog precoder and digital precoder paving the way for the development of an iterative algorithm to locate its Karush-Kuhn-Tucker (KKT) solution. Finally, we show that our proposed joint design algorithm can be adapted to handle the multi-antenna Willie scenario and simulation results show that our proposed joint design algorithms can achieve significantly better performance as compared with some benchmark schemes.

Considering multi-user downlink ultra-high reliability and low latency communications (URLLC), this paper employs the artificial noise (AN) technique to establish a secure and covert broadcast communication paradigm for the first time. Specifically, a multi-antenna transmitter (Alice) broadcasts the confidential information to multiple legitimate users in the presence of a multi-antenna malicious warden (Willie) and a multi-antenna eavesdropper (Eve). It is well known that AN is an effective technique for securing the physical layer security (PLS) of signal transmissions. Nevertheless, AN emission also exposes the signal transmission and decreases the signal covertness. Taking into account the impact of short-packet URLLC transmissions, we investigate the joint optimization of the precoder and AN to maximize the secrecy rate under the covertness constraint. Although the considered problem is nonconvex, we propose a branch-reduce-and-bound (BRB)-based algorithm to solve it optimally. However, the nested-loop structure of the BRB-based algorithm incurs a high computational complexity. To strike a balance between the performance and computational complexity, we also propose a low-complexity penalty successive convex approximation (SCA)-based algorithm, whose performance approaches that of the optimal BRB-based algorithm, particularly in the low to medium transmit power regime. Simulation results demonstrate the excellent performance of our proposed optimization algorithms compared with various benchmark algorithms and unveil the importance of exploiting AN for secrecy provisioning.

This paper investigates resource allocation design for intelligent reflecting surface (IRS)-aided joint processing coordinated multipoint (JP-CoMP) downlink cellular networks with underlaying device-to-device (D2D) communications. In particular, an IRS is employed to establish favorable communication channel conditions and to mitigate the malignant interference caused by D2D devices. We aim to maximize the system sum-rate by jointly designing the cellular user (CU) association, the active beamforming at the base stations (BSs), the passive beamforming at the IRS, and the transmit power of each D2D transmitter (DT). The resource allocation design is formulated as a non-convex optimization problem while taking into account the quality of service (QoS) requirement of CUs, the power allocations for both CUs and D2D pairs, and the limited backhaul capacity. To handle the non-convex optimization problem, we propose a computationally efficient iterative algorithm exploiting the big-M formulation, the penalty method, and the successive convex approximation, which is guaranteed to converge to a Karush-Kuhn-Tucker (KKT) solution. Simulation results demonstrate that the proposed scheme can increase the system sum-rate by 70% and 20% compared with the schemes with no IRS and random phase shifts, respectively, when the minimum required SINR of CUs is 5 dB. Additionally, our results confirm the superiority of introducing IRS for harnessing interference in wireless communication systems.

In this paper, we investigate the optimal design of a wireless-powered covert communication (WP-CC) system, where a full-duplex (FD) receiver transmits artificial noise (AN) to simultaneously charge an energy-constrained transmitter and to confuse a warden’s detection on the transmitter’s communication activity. In order to achieve a higher level of covertness, we propose a probabilistic accumulate-then-transmit (ATT) protocol, where the transmitter is able to adjust the prior probability conditioned on the available energy being sufficient, i.e.,$p$, rather than setting$p=1$as in the traditional ATT protocol to maximize the system throughput. Then, we derive the warden’s minimum detection error probability and characterize the effective covert rate from the transmitter to the receiver to quantify the communication covertness and quality, respectively. The derived analytical results facilitate the joint optimization of the probability$p$and the information transmit power to maximize the communication covertness subject to a quality-of-service (QoS) requirement on communication. We further present the optimal design of a cable-powered covert communication (CP-CC) system as a benchmark for comparison. Our simulation shows that the proposed probabilistic ATT protocol (with a varying$p$) can achieve the covertness upper bound determined by the CP-CC system, while the traditional ATT protocol (with$p=1$) cannot, which confirms the benefits brought by the proposed probabilistic ATT protocol in covert communications.

This paper proposes an off-grid channel estimation scheme for orthogonal time-frequency space (OTFS) systems adopting the sparse Bayesian learning (SBL) framework. To avoid channel spreading caused by the fractional delay and Doppler shifts and to fully exploit the channel sparsity in the delay-Doppler (DD) domain, we estimate the original DD domain channel response rather than the effective DD domain channel response as commonly adopted in the literature. OTFS channel estimation is firstly formulated as a one-dimensional (1D) off-grid sparse signal recovery (SSR) problem based on a virtual sampling grid defined in the DD space, where the on-grid and off-grid components of the delay and Doppler shifts are separated for estimation. In particular, the on-grid components of the delay and Doppler shifts are jointly determined by the entry indices with significant values in the recovered sparse vector. Then, the corresponding off-grid components are modeled as hyper-parameters in the proposed SBL framework, which can be estimated via the expectation-maximization method. To strike a balance between channel estimation performance and computational complexity, we further propose a two-dimensional (2D) off-grid SSR problem via decoupling the delay and Doppler shift estimations. In our developed 1D and 2D off-grid SBL-based channel estimation algorithms, the hyper-parameters are updated alternatively for computing the conditional posterior distribution of channels, which can be exploited to reconstruct the effective DD domain channel. Compared with the 1D method, the proposed 2D method enjoys a much lower computational complexity while only suffers a slight performance degradation. Simulation results verify the superior performance of the proposed channel estimation schemes over state-of-the-art schemes.

In order to fully exploit the advantages of massive multiple-input multiple-output (mMIMO), it is critical for the transmitter to accurately acquire the channel state information (CSI). Deep learning (DL)-based methods have been proposed for CSI compression and feedback to the transmitter. Although most existing DL-based methods consider the CSI matrix as an image, structural features of the CSI image are rarely exploited in neural network design. As such, we propose a model of self-information that dynamically measures the amount of information contained in each patch of a CSI image from the perspective of structural features. Then, by applying the self-information model, we propose a model-and-data-driven network for CSI compression and feedback, namely IdasNet. The IdasNet includes the design of a module of self-information deletion and selection (IDAS), an encoder of informative feature compression (IFC), and a decoder of informative feature recovery (IFR). In particular, the model-driven module of IDAS pre-compresses the CSI image by removing informative redundancy in terms of the self-information. The encoder of IFC then conducts feature compression to the pre-compressed CSI image and generates a feature codeword which contains two components, i.e., codeword values and position indices of the codeword values. Subsequently, the IFR decoder decouples the codeword values as well as position indices to recover the CSI image. Experimental results verify that the proposed IdasNet noticeably outperforms existing DL-based networks under various compression ratios while it has the number of network parameters reduced by orders-of-magnitude compared with various existing methods.

This work examines the performance gain achieved by deploying an intelligent reflecting surface (IRS) in covert communications. To this end, we formulate the joint design of the transmit power and the IRS reflection coefficients by taking into account the communication covertness for the cases with global channel state information (CSI) and without a warden’s instantaneous CSI. For the case of global CSI, we first prove that perfect covertness is achievable with the aid of the IRS even for a single-antenna transmitter, which is impossible without an IRS. Then, we develop a penalty successive convex approximation (PSCA) algorithm to tackle the design problem. Considering the high complexity of the PSCA algorithm, we further propose a low-complexity two-stage algorithm, where analytical expressions for the transmit power and the IRS’s reflection coefficients are derived. For the case without the warden’s instantaneous CSI, we first derive the covertness constraint analytically facilitating the optimal phase shift design. Then, we consider three hardware-related constraints on the IRS’s reflection amplitudes and determine their optimal designs together with the optimal transmit power. Our examination shows that significant performance gain can be achieved by deploying an IRS into covert communications.

This work examines the performance gain achieved by deploying an intelligent reflecting surface (IRS) for delay-constrained covert communications. To this end, we formulate the joint design of the transmit power and the IRS reflection coefficients, including its phase shifts and reflection amplitudes, to maximize the communication quality subject to a covertness constraint. We first prove that perfect covertness is achievable with the aid of the IRS even for a single-antenna transmitter, which is impossible without the IRS. Then, we develop a penalty-based successive convex approximation (PSCA) algorithm to tackle the design optimization problem. Considering the high complexity of the PSCA algorithm, we further propose a low-complexity two-stage algorithm, where closed-form expressions for the transmit power and the IRS's reflection coefficients are derived. Our examination shows that significant performance gain can be achieved by deploying an IRS into covert communications.

Reconfigurable intelligent surface (RIS) assisted radio is considered as an enabling technology with great potential for the sixth-generation (6G) wireless communications standard. The achievable secrecy rate (ASR) is one of the most fundamental metrics to evaluate the capability of facilitating secure communication for RIS-assisted systems. However, the definition of ASR is based on Shannon's information theory, which generally requires long codewords and thus fails to quantify the secrecy of emerging delay-critical services. Motivated by this, in this paper we investigate the problem of maximizing the secrecy rate under a delay-limited quality-of-service (QoS) constraint, termed as the effective secrecy rate (ESR), for an RIS-assisted multiple-input single-output (MISO) wiretap channel subject to a transmit power constraint. We propose an iterative method to find a stationary solution to the formulated non-convex optimization problem using a block coordinate ascent method (BCAM), where both the beamforming vector at the transmitter as well as the phase shifts at the RIS are obtained in closed forms in each iteration. We also present a convergence proof, an efficient implementation, and the associated complexity analysis for the proposed method. Our numerical results demonstrate that the proposed optimization algorithm converges significantly faster that an existing solution. The simulation results also confirm that the secrecy rate performance of the system with stringent delay requirements reduces significantly compared to the system without any delay constraints, and that this reduction can be significantly mitigated by an appropriately placed large-size RIS.

The realization of practical intelligent reflecting surface (IRS)-assisted multi-user communication (IRS-MUC) systems critically depends on the proper beamforming design exploiting accurate channel state information (CSI). However, channel estimation (CE) in IRS-MUC systems requires a significantly large training overhead due to the numerous reflection elements involved in IRS. In this paper, we adopt a deep learning approach to implicitly learn the historical channel features and directly predict the IRS phase shifts for the next time slot to maximize the average achievable sum-rate of an IRS-MUC system taking into account the user mobility. By doing this, only a low-dimension multiple-input single-output (MISO) CE is needed for transmit beamforming design, thus significantly reducing the CE overhead. To this end, a location-aware convolutional long short-term memory network (LA-CLNet) is first developed to facilitate predictive beamforming at IRS, where the convolutional and recurrent units are jointly adopted to exploit both the spatial and temporal features of channels simultaneously. Given the predictive IRS phase shift beamforming, an instantaneous CSI (ICSI)-aware fully-connected neural network (IA-FNN) is then proposed to optimize the transmit beamforming matrix at the access point. Simulation results demonstrate that the sum-rate performance achieved by the proposed method approaches that of the genie-aided scheme with the full perfect ICSI.

This paper investigates the problem of joint massive devices separation and channel estimation for a reconfigurable intelligent surface (RIS)-aided unsourced random access (URA) scheme in the sixth-generation (6G) wireless networks. In particular, by associating the data sequences to a rank-one tensor and exploiting the angular sparsity of the channel, the detection problem is cast as a high-order coupled tensor decomposition problem. However, the coupling among multiple devices to RIS (device-RIS) channels together with their sparse structure make the problem intractable. By devising novel priors to incorporate problem structures, we design a novel probabilistic model to capture both the element-wise sparsity from the angular channel model and the low rank property due to the sporadic nature of URA. Based on the this probabilistic model, we develop a coupled tensor-based automatic detection (CTAD) algorithm under the framework of variational inference with fast convergence and low computational complexity. Moreover, the proposed algorithm can automatically learn the number of active devices and thus effectively avoid noise overfitting. Extensive simulation results confirm the effectiveness and improvements of the proposed URA algorithm in large-scale RIS regime.

Non-orthogonal multiple access (NOMA) serves multiple users simultaneously via the same resource block by exploiting superposition coding at the transmitter and successive interference cancellation (SIC) at the receivers. Under practical considerations, perfect SIC may not be achieved. Thus, residual interference (RI) occurs inevitably due to imperfect SIC. In this work, we first propose a novel model for characterizing RI to provide a more realistic secrecy performance analysis of a downlink NOMA system under imperfect SIC at receivers. In the presence of untrusted users, NOMA has an inherent security flaw. Therefore, for this untrusted users' scenario, we derive new analytical expressions of secrecy outage probability (SOP) for each user in a two-user untrusted NOMA system by using the proposed RI model. To further shed light on the obtained results and obtain a deeper understanding, a high signal-to-noise ratio approximation of the SOPs are also obtained. Lastly, numerical investigations are provided to validate the accuracy of the desired analytical results and present valuable insights into the impact of various system parameters on the secrecy rate performance of the secure NOMA communication system.

This paper considers the physical layer security (PLS) and covertness of the signal transmission in a multiple-input single-output downlink adopting ultra-high reliability and low latency communication (URLLC). In the considered system, Alice transmits confidential signals to Bob in the presence of a multi-antenna eavesdropper (Eve) and a multi-antenna watchful adversary (Willie). Although artificial noise (AN) is a common PLS technique for protecting the confidential signal from wiretapping, it may reduce the communication covertness due to the additional signal emission. For maximizing the achievable secrecy rate, we propose an AN-aided secure and covert communication strategy through optimizing the information carrying beamformer and AN jointly subject to the covertness constraint. To tackle the formulated non-convex design problem, we propose a branch-reduce-and-bound (BRB)-based algorithm to solve the considered problem globally. Simulation results validate its efficiency compared with a benchmark algorithm and unveil the importance of exploiting AN.

Wireless federated learning (FL) is an emerging machine learning paradigm that trains a global parametric model from distributed datasets via wireless communications. This paper proposes a unit-modulus wireless FL (UMWFL) framework, which simultaneously uploads local model parameters and computes global model parameters via optimized phase shifting. The proposed framework avoids sophisticated baseband signal processing, leading to both low communication delays and implementation costs. A training loss bound is derived and a penalty alternating minimization (PAM) algorithm is proposed to minimize the nonconvex nonsmooth loss bound. Experimental results in the Car Learning to Act (CARLA) platform show that the proposed UMWFL framework with PAM algorithm achieves smaller training losses and testing errors than those of the benchmark scheme.

This paper proposes an off-grid channel estimation scheme for orthogonal time-frequency space (OTFS) systems adopting the sparse Bayesian learning (SBL) framework. To avoid channel spreading caused by the fractional delay and Doppler shifts and to fully exploit the channel sparsity in the delay-Doppler (DD) domain, we estimate the original DD domain channel response rather than the effective DD domain channel response as commonly adopted in the literature. The OTFS channel estimation problem is formulated as an off-grid sparse signal recovery problem based on a virtual sampling grid defined in the DD space, where the on-grid and off-grid components of the delay and Doppler shifts are separated for estimation. In particular, the on-grid components of the delay and Doppler shifts are jointly determined by the entry indices with significant values in the recovered sparse vector. Then, the corresponding off-grid components are modeled as hyper-parameters in the proposed SBL framework, which can be estimated via the expectation-maximization method. Simulation results verify that compared with the on-grid approach, our proposed off-grid OTFS channel estimation scheme enjoys a 1.5 dB lower normalized mean square error.

In this paper, we study the optimal transmission of a multi-quality tiled 360 virtual reality (VR) video from a multi-antenna server (e.g., access point or base station) to multiple single-antenna users in a multiple-input multiple-output (MIMO)-orthogonal frequency division multiple access (OFDMA) system. We minimize the total transmission power with respect to the subcarrier allocation constraints, rate allocation constraints, and successful transmission constraints, by optimizing the beamforming vector and subcarrier, transmission power and rate allocation. The formulated resource allocation problem is a challenging mixed discrete-continuous optimization problem. We obtain an asymptotically optimal solution in the case of a large antenna array, and a suboptimal solution in the general case. As far as we know, this is the first work providing optimization-based design for 360 VR video transmission in MIMO-OFDMA systems. Finally, by numerical results, we show that the proposed solutions achieve significant improvement in performance compared to the existing solutions.

Channel estimation is of great importance in realizing practical intelligent reflecting surface-assisted multi-user communication (IRS-MC) systems. However, different from traditional communication systems, an IRS-MC system generally involves a cascaded channel with a sophisticated statistical distribution, which hinders the implementations of the Bayesian estimators. To further improve the channel estimation performance, in this paper, we model the channel estimation as a denoising problem and adopt a data-driven approach to realize the channel estimation. Specifically, we propose a convolutional neural network (CNN)-based deep residual network (CDRN) to implicitly learn the residual noise for recovering the channel coefficients from the noisy pilot-based observations. In the proposed CDRN, a CNN denoising block equipped with an element-wise subtraction structure is designed to exploit both the spatial features of the noisy channel matrices and the additive nature of the noise simultaneously, which further improves the estimation accuracy. Simulation results demonstrate that the proposed method can almost achieve the same estimation accuracy as that of the optimal minimum mean square error (MMSE) estimator requiring the knowledge of the channel distribution.

Wireless Internet-of-Things (IoT) networks empowered by blockchain have became a promising architecture to establish trust and consensus mechanisms in a distributed manner. However, the computational complexity and limited on-board energy of wireless devices impose great challenges on applying blockchain into IoT networks. To address this issue, this work introduces multiple mobile edge computing (MEC) to provide sufficient computational power for miners (i.e., IoT devices). As such, the computation-intensive tasks of the miners can be either computed locally or offloaded to some certain MEC servers to fully exploit the computation resources. Moreover, to decrease the energy consumption of the IoT networks, an optimization problem is formulated to maximize the energy efficiency of IoT devices by jointly optimizing the computation mode selection and power allocation. Since the formulated problem is generally intractable with mixed-integer variables, an Fmincon-based algorithm is proposed, which guarantees a globally optimal solution. To further reduce the computational complexity of the proposed optimal method, a Deep Neural Network (DNN)-based deep learning method is applied to facilitate the computation of the proposed algorithm. Finally, numerical results demonstrate the advantages of the proposed network architecture and the algorithm in terms of both the energy and computational efficiency.

In this paper, we investigate the impacts of transmitter and receiver windows on orthogonal time-frequency space (OTFS) modulation and propose a window design to improve the OTFS channel estimation performance. Assuming ideal pulse shaping filters at the transceiver, we first identify the role of window in effective channel and the reduced channel sparsity with conventional rectangular window. Then, we characterize the impacts of windowing on the effective channel estimation performance for OTFS modulation. Based on the revealed insights, we propose to apply a Dolph-Chebyshev (DC) window at either the transmitter or the receiver to effectively enhance the sparsity of the effective channel. As such, the channel spread due to the fractional Doppler is significantly reduced, which leads to a lower error floor in channel estimation compared with that of the rectangular window. Simulation results verify the accuracy of the obtained analytical results and confirm the superiority of the proposed window designs in improving the channel estimation performance over the conventional rectangular or Sine windows.

Non-orthogonal multiple access (NOMA) has been widely recognized as a promising multiple access scheme for realizing next generation wireless communications. Unlike existing NOMA schemes assuming perfectly time synchronized user's signals received at the base station (BS), in this paper, we investigate the achievable rates of uplink NOMA with asynchronized transmission. By invoking Szegö's Theorem, we derive both the upper- and lower-bounds of the achievable rates of asynchronized NOMA (aNOMA) systems. In particular, we reveal that the derived lower-bound is essentially the achievable rate for conventional synchronized NOMA systems, which indicates that the asynchronization is not necessarily a foe. More specifically, we show that aNOMA systems are superior to conventional NOMA systems in terms of the achievable rates with non-sinc shaping pulses. Important insights are also unveiled based on the derived bounds. Simulation results confirm the validity of our derived analysis and demonstrate considerable achievable rates gains of aNOMA systems over conventional NOMA systems.

In this paper, we investigate resource allocation algorithm design for large intelligent reflecting surface (IRS)assisted simultaneous wireless information and power transfer (SWIPT) systems. To this end, we adopt a physics-based IRS model that, unlike the conventional IRS model, takes into account the impact of the incident and reflection angles of the impinging electromagnetic wave on the reflected signal. To facilitate efficient resource allocation design for large IRSs, we employ a scalable optimization framework, where the IRS is partitioned into several tiles and the phase shift elements of each tile are jointly designed to realize different transmission modes. Then, the beamforming vectors at the base station (BS) and the transmission mode selection of the tiles of the IRS are jointly optimized for minimization of the BS transmit power taking into account the quality-of-service requirements of both non-linear energy harvesting receivers and information decoding receivers. For handling the resulting non-convex optimization problem, we apply a penalty-based method, successive convex approximation, and semidefinite relaxation to develop a computationally efficient algorithm which asymptotically converges to a locally optimal solution of the considered problem. Our simulation results show that the proposed scheme enables considerable power savings compared to two baseline schemes. Moreover, our results also illustrate that the advocated physics-based model and scalable optimization framework for large IRSs allows us to strike a balance between system performance and computational complexity, which is vital for realizing large IRS-assisted communication systems.

Massive access, also known as massive connectivity or massive machine-type communication (mMTC), is one of the main use cases of the fifth-generation (5G) and beyond 5G (B5G) wireless networks. A typical application of massive access is the cellular Internet of Things (IoT). Different from conventional human-type communication, massive access aims at realizing efficient and reliable communications for a massive number of IoT devices. Hence, the main characteristics of massive access include low power, massive connectivity, and broad coverage, which require new concepts, theories, and paradigms for the design of next-generation cellular networks. This paper presents a comprehensive survey of massive access design for B5G wireless networks. Specifically, we provide a detailed review of massive access from the perspectives of theory, protocols, techniques, coverage, energy, and security. Furthermore, several future research directions and challenges are identified.

The emerging space-air-ground integrated network has attracted intensive research and necessitates reliable and efficient aeronautical communications. This paper investigates terahertz Ultra-Massive (UM)-MIMO-based aeronautical communications and proposes an effective channel estimation and tracking scheme, which can solve the performance degradation problem caused by the uniquetriple delay-beam-Doppler squint effectsof aeronautical terahertz UM-MIMO channels. Specifically, based on the rough angle estimates acquired from navigation information, an initial aeronautical link is established, where the delay-beam squint at transceiver can be significantly mitigated by employing a Grouping True-Time Delay Unit (GTTDU) module (e.g., the designedRotman lens-based GTTDU module). According to the proposed prior-aided iterative angle estimation algorithm, azimuth/elevation angles can be estimated, and these angles are adopted to achieve precise beam-alignment and refine GTTDU module for further eliminating delay-beam squint. Doppler shifts can be subsequently estimated using the proposed prior-aided iterative Doppler shift estimation algorithm. On this basis, path delays and channel gains can be estimated accurately, where the Doppler squint can be effectively attenuated via compensation process. For data transmission, a data-aided decision-directed based channel tracking algorithm is developed to track the beam-aligned effective channels. When the data-aided channel tracking is invalid, angles will be re-estimated at the pilot-aided channel tracking stage with an equivalent sparse digital array, where angle ambiguity can be resolved based on the previously estimated angles. The simulation results and the derived Cramér-Rao lower bounds verify the effectiveness of our solution.

Wireless communication is an essential technology to unlock the full potential of unmanned aerial vehicles (UAVs) in numerous applications and has thus received unprecedented attention recently. Although technologies such as direct link, WiFi, and satellite communications are still useful in some remote scenarios where cellular services are unavailable, it is believed that exploiting the thriving 5G and beyond cellular networks to support UAV communications is the most promising and cost-effective approach, especially when the number of UAVs grows dramatically. On the one hand, to guarantee safe and efficient flight operations of multiple UAVs, it is of paramount importance to provide secure and ultra-reliable communication links between the UAVs and their ground pilots or control stations for conveying command and control signals, especially in beyond-visual-line-of-sight (BVLOS) scenarios. On the other hand, because of advances in communication equipment miniaturization as well as UAV manufacturing, mounting compact and lightweight base stations (BSs) or relays on UAVs becomes increasingly feasible. This has led to two promising research paradigms for UAV communications, namely, UAV-assisted cellular communications and cellular-connected UAVs, where UAVs are integrated into cellular networks as aerial communication platforms and aerial users, respectively. As such, integrating UAVs into cellular networks is believed to be a win-win technology for both UAV-related industries and cellular network operators, which not only creates plenty of new business opportunities but also benefits the communication performance of 3-D wireless networks. In addition, UAV related sensing and computing are also helpful for achieving efficient and reliable communication (e.g., in avoiding coverage holes) as well as smart UAV coordination, positioning, and trajectory design. However, 5G and beyond wireless networks with UAVs significantly differs from traditional communication systems, because of the high altitude and high maneuverability of UAVs, the unique UAV-ground channels, the diversified quality of service (QoS) requirements for downlink command and control (C&C) and uplink mission-related data transmission, the stringent constraints imposed by the size, weight, and power (SWAP) limitations of UAVs, as well as the new design degrees of freedom enabled by joint UAV mobility control and communication resource allocation.

Due to the advancements in cellular technologies and the dense deployment of cellular infrastructure, integrating unmanned aerial vehicles (UAVs) into the fifth-generation (5G) and beyond cellular networks is a promising solution to achieve safe UAV operation as well as enabling diversified applications with mission-specific payload data delivery. In particular, 5G networks need to support three typical usage scenarios, namely, enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC). On the one hand, UAVs can be leveraged as cost-effective aerial platforms to provide ground users with enhanced communication services by exploiting their high cruising altitude and controllable maneuverability in three-dimensional (3D) space. On the other hand, providing such communication services simultaneously for both UAV and ground users poses new challenges due to the need for ubiquitous 3D signal coverage as well as the strong air-ground network interference. Besides the requirement of high-performance wireless communications, the ability to support effective and efficient sensing as well as network intelligence is also essential for 5G-and-beyond 3D heterogeneous wireless networks with coexisting aerial and ground users. In this paper, we provide a comprehensive overview of the latest research efforts on integrating UAVs into cellular networks, with an emphasis on how to exploit advanced techniques (e.g., intelligent reflecting surface, short packet transmission, energy harvesting, joint communication and radar sensing, and edge intelligence) to meet the diversified service requirements of next-generation wireless systems. Moreover, we highlight important directions for further investigation in future work.

Wireless communication is an essential technology to unlock the full potential of unmanned aerial vehicles (UAVs) in numerous applications and has thus received unprecedented attention recently. Although technologies such as direct link, WiFi, and satellite communications are still useful in some remote scenarios where cellular services are unavailable, it is believed that exploiting the thriving 5G and beyond cellular networks to support UAV communications is the most promising and cost-effective approach, especially when the number of UAVs grows dramatically. On the one hand, to guarantee safe and efficient flight operations of multiple UAVs, it is of paramount importance to provide secure and ultra-reliable communication links between the UAVs and their ground pilots or control stations for conveying command and control signals, especially in beyond-visual-line-of-sight (BVLOS) scenarios. On the other hand, because of advances in communication equipment miniaturization as well as UAV manufacturing, mounting compact and lightweight base stations (BSs) or relays on UAVs becomes increasingly feasible. This has led to two promising research paradigms for UAV communications, namely, UAV-assisted cellular communications and cellular-connected UAVs, where UAVs are integrated into cellular networks as aerial communication platforms and aerial users, respectively. As such, integrating UAVs into cellular networks is believed to be a win-win technology for both UAV-related industries and cellular network operators, which not only creates plenty of new business opportunities but also benefits the communication performance of 3-D wireless networks. In addition, UAV related sensing and computing are also helpful for achieving efficient and reliable communication (e.g., in avoiding coverage holes) as well as smart UAV coordination, positioning, and trajectory design. However, 5G and beyond wireless networks with UAVs significantly differs from traditional communication systems, because of the high altitude and high maneuverability of UAVs, the unique UAV-ground channels, the diversified quality of service (QoS) requirements for downlink command and control (C&C) and uplink mission-related data transmission, the stringent constraints imposed by the size, weight, and power (SWAP) limitations of UAVs, as well as the new design degrees of freedom enabled by joint UAV mobility control and communication resource allocation.

This paper investigates robust and secure multiuser multiple-input single-output (MISO) downlink communications assisted by a self-sustainable intelligent reflection surface (IRS), which can simultaneously reflect and harvest energy from the received signals. We study the joint design of beamformers at an access point (AP) and the phase shifts as well as the energy harvesting schedule at the IRS for maximizing the system sum-rate. The design is formulated as a non-convex optimization problem taking into account the wireless energy harvesting capability of IRS elements, secure communications, and the robustness against the impact of channel state information (CSI) imperfection. Subsequently, we propose a computationally-efficient iterative algorithm to obtain a suboptimal solution to the design problem. In each iteration,$\mathcal {S}$-procedure and the successive convex approximation are adopted to handle the intermediate optimization problem. Our simulation results unveil that: 1) there is a non-trivial trade-off between the system sum-rate and the self-sustainability of the IRS; 2) the performance gain achieved by the proposed scheme is saturated with a large number of energy harvesting IRS elements; 3) an IRS equipped with small bit-resolution discrete phase shifters is sufficient to achieve a considerable system sum-rate of the ideal case with continuous phase shifts.

This article investigates intelligent reflecting surface (IRS)-aided multicell wireless networks, where an IRS is deployed to assist the joint processing coordinated multipoint (JP-CoMP) transmission from multiple base stations (BSs) to multiple cell-edge users. By taking into account the fairness among cell-edge users, we aim at maximizing the minimum achievable rate of cell-edge users by jointly optimizing the transmit beamforming at the BSs and the phase shifts at the IRS. As a compromise approach, we transform the non-convex max-min problem into an equivalent form based on the mean-square error method, which facilities the design of an efficient suboptimal iterative algorithm. In addition, we investigate two scenarios, namely the single-user system and the multiuser system. For the former scenario, the optimal transmit beamforming is obtained based on the dual subgradient method, while the phase shift matrix is optimized based on the Majorization-Minimization method. For the latter scenario, the transmit beamforming matrix and phase shift matrix are obtained by the second-order cone programming and semidefinite relaxation techniques, respectively. Numerical results demonstrate the significant performance improvement achieved by deploying an IRS. Furthermore, the proposed JP-CoMP design significantly outperforms the conventional coordinated scheduling/coordinated beamforming coordinated multipoint (CS/CB-CoMP) design in terms of max-min rate.

With practical finite alphabet inputs, usually the throughput of a practical communication system cannot reach the capacity based on assumption of Gaussian inputs. In this paper, we study the resource allocation strategy for multiple-input multiple-output (MIMO) full-duplex backscatter assisted wireless-powered communication network (FD-BAWPCN) with finite alphabet inputs, to maximize the sum-throughput. Firstly, we propose a gradient-based resource allocation strategy (GBRA), which alternately optimizes the precoder and time allocation based on the two-block alternating direction method of multipliers (ADMM). Secondly, to reduce the computational complexity and improve the feasibility of the strategy in practical applications, we further propose a codebook-based resource allocation strategy (CBRA), which can run more than three orders of magnitude faster than GBRA at the expense of a small sum-rate degradation. Then, the performance of the full-duplex (FD) and half-duplex (HD) system with or without backscatter assistance using GBRA are compared and analyzed. Numerical results demonstrate that the GBRA strategy has great robustness under various conditions but suffers a high computational complexity, and the CBRA strategy is effective and converges speedily.

Wireless body area network (WBAN) has become a promising technology, which can be widely applied in health monitoring, and so on. However, the performance of a practical WBAN may severely suffer from the degradation caused by dynamic nature of wireless channels with the movements of human body. Traditional communication frameworks cannot catch up with the channel variation of dynamic WBANs, which may severely degrade the performance, so an accurate channel prediction model is necessary for developing an efficient transmission strategy. In this paper, we propose a DeepBAN communication framework for dynamic WBANs. In our proposed framework, a temporal convolution network (TCN) based deep learning approach is adopted for channel prediction, the computationally intensive task of which is processed by mobile edge computing (MEC), to reduce the response time. Given the predicted channel conditions, we propose a joint power control, time-slot allocation, and relay selection algorithm to maximize the energy efficiency of the system, taking into account the transmission reliability and end-to-end latency requirements. We evaluate the performance of DeepBAN, and the results show that it can achieve energy-efficient, reliable, and low-latency data transmission in dynamic WBANs, which can improve the system energy efficiency by 15% compared with the stochastic scheduling scheme.

In this paper, we focus on intelligent reflecting surface (IRS) assisted multi-antenna communications with transceiver hardware impairments encountered in practice. In particular, we aim to maximize the received signal-to-noise ratio (SNR) taking into account the impact of hardware impairments, where the source transmit beamforming and the IRS reflect beamforming are jointly designed under the proposed optimization framework. To circumvent the non-convexity of the formulated design problem, we first derive a closed-form optimal solution to the source transmit beamforming. Then, for the optimization of IRS reflect beamforming, we obtain an upper bound to the optimal objective value via solving a single convex problem. A low-complexity minorization-maximization (MM) algorithm was developed to approach the upper bound. Simulation results demonstrate that the proposed beamforming design is more robust to the hardware impairments than that of the conventional SNR maximized scheme. Moreover, compared to the scenario without deploying an IRS, the performance gain brought by incorporating the hardware impairments is more evident for the IRS-aided communications.

This article investigates the intelligent reflecting surface (IRS)-aided multi-antenna covert communications. In particular, with the help of an IRS, a favorable communication environment can be established via controllable intelligent signal reflection, which facilitates the covert communication between a multi-antenna transmitter (Alice) and a legitimate full-duplex receiver (Bob) in the existence of a watchful warden (Willie). In order to shelter the desired communication, Bob generates jamming signals with a varying power to confuse Willie. The beamforming vector employed by Alice and the passive phase shifts of the IRS are optimized jointly to maximize the covert rate under the constraints of the successful detection probability at Willie and the communication outage experienced by Bob. We focus on the worst case by characterizing the minimum successful detection probability at Willie. The formulated problem is non-convex, due to the coupling between the beamforming vector of Alice and the phase shifts of the IRS, and the unit modulus constraint on the phase shifts of the IRS. To tackle the above issues, we first employ the penalty dual decomposition (PDD) method to handle the coupling effect. After that, we apply the successive convex approximation (SCA) method to develop an iterative algorithm for locating a Karush-Kuhn-Tucker (KKT) solution of the joint design problem. Moreover, we show that our proposed iterative algorithm can be adapted to handle the multi-antenna Willie case. Simulation results validate the effectiveness of the proposed iterative algorithm and show the great potential brought by the IRS for covert communications.

In this paper, we investigate the impacts of transmitter and receiver windows on the performance of orthogonal time-frequency space (OTFS) modulation and propose window designs to improve the OTFS channel estimation and data detection performance. In particular, assuming ideal pulse shaping filters at the transceiver, we derive the impacts of windowing on the effective channel and its estimation performance in the delay-Doppler (DD) domain, the total average transmit power, and the effective noise covariance matrix. When the channel state information (CSI) is available at the transceiver, we analyze the minimum squared error (MSE) of data detection and propose an optimal transmitter window to minimize the detection MSE. The proposed optimal transmitter window can be interpreted as a mercury/water-filling power allocation scheme, where the mercury is firstly filled before pouring water to pre-equalize the time-frequency (TF) domain channels. When the CSI is not available at the transmitter but can be estimated at the receiver, we propose to apply a Dolph-Chebyshev (DC) window at either the transmitter or the receiver, which can effectively enhance the sparsity of the effective channel in the DD domain. Thanks to the enhanced DD domain channel sparsity, the channel spread due to the fractional Doppler is significantly reduced, which leads to a lower error floor in both channel estimation and data detection compared with that of rectangular window. Simulation results verify the accuracy of the obtained analytical results and confirm the superiority of the proposed window designs in improving the channel estimation and data detection performance over the conventional rectangular window design.

In this paper, we investigate resource allocation for IRS-assisted green multiuser multiple-input single-output (MISO) systems. To minimize the total transmit power, both the beamforming vectors at the access point (AP) and the phase shifts at multiple IRSs are jointly optimized, while taking into account the minimum required quality-of-service (QoS) of multiple users. First, two novel algorithms, namely a penalty-based alternating minimization (AltMin) algorithm and an inner approximation (IA) algorithm, are developed to tackle the non-convexity of the formulated optimization problem when perfect channel state information (CSI) is available. Existing designs employ semidefinite relaxation in AltMin-based algorithms, which, however, cannot ensure convergence. In contrast, the proposed penalty-based AltMin and IA algorithms are guaranteed to converge to a stationary point and a Karush-Kuhn-Tucker (KKT) solution of the design problem, respectively. Second, the impact of imperfect knowledge of the CSI of the channels between the AP and the users is investigated. To this end, a non-convex robust optimization problem is formulated and the penalty-based AltMin algorithm is extended to obtain a stationary solution. Simulation results reveal a key trade-off between the speed of convergence and the achievable total transmit power for the two proposed algorithms. In addition, we show that the proposed algorithms can significantly reduce the total transmit power at the AP compared to various baseline schemes and that the optimal numbers of transmit antennas and IRS reflecting elements, which maximize the system energy efficiency of the considered system, are finite.

Over-the-air computation (AirComp) has been recognized as a promising technique in Internet-of-Things (IoT) networks for fast data aggregation from a large number of wireless devices. However, the computation accuracy of AirComp highly depends on the devices with the worst channels condition, which degrades severely when the number of devices becomes large. To address this issue, we exploit the massive multiple-input multiple-output (MIMO) with hybrid beamforming, in order to enhance the computational accuracy of AirComp in a cost-effective manner. In particular, we consider the scenario with a large number of multi-antenna devices simultaneously sending data to an access point (AP) equipped with massive antennas for functional computation over the air. Under this setup, we jointly optimize the transmit digital beamforming at the wireless devices and the receive hybrid beamforming at the AP, with the objective of minimizing the computational mean-squared error (MSE) subject to the individual transmit power constraints at the wireless devices. To solve the non-convex hybrid beamforming design optimization problem, we propose an alternating-optimization-based approach, in which the transmit digital beamforming and the receive analog and digital beamforming are optimized in an alternating manner. In particular, we propose two computationally efficient algorithms to handle the challenging receive analog beamforming problem, by exploiting the techniques of successive convex approximation (SCA) and coordinate descent (CD), respectively. It is shown that for the special case with a fully-digital receiver at the AP, the achieved MSE of the massive MIMO AirComp system is inversely proportional to the number of receive antennas. Furthermore, numerical results show that the proposed hybrid beamforming design substantially enhances the computation MSE performance as compared to other benchmark schemes, while the SCA-based algorithm performs closely to the performance upper bound achieved by the fully-digital beamforming.

Radio-frequency fingerprints (RFFs) are promising solutions for realizing low-cost physical layer authentication. Machine learning-based methods have been proposed for RFF extraction and discrimination. However, most existing methods are designed for the closed-set scenario where the set of devices is remains unchanged. These methods can not be generalized to the RFF discrimination of unknown devices. To enable the discrimination of RFF from both known and unknown devices, we propose a new end-to-end deep learning framework for extracting RFFs from raw received signals. The proposed framework comprises a novel preprocessing module, called neural synchronization (NS), which incorporates the data-driven learning with signal processing priors as an inductive bias from communication-model based processing. Compared to traditional carrier synchronization techniques, which are static, this module estimates offsets by two learnable deep neural networks jointly trained by the RFF extractor. Additionally, a hypersphere representation is proposed to further improve the discrimination of RFF. Theoretical analysis shows that such a data-and-model framework can better optimize the mutual information between device identity and the RFF, which naturally leads to better performance. Experimental results verify that the proposed RFF significantly outperforms purely data-driven DNN-design and existing handcrafted RFF methods in terms of both discrimination and network generalizability.

Reconfigurable intelligent surface (RIS)-aided wireless communications have drawn significant attention recently. We study the physical layer security of the downlink RIS-aided transmission framework for randomly located users in the presence of a multi-antenna eavesdropper. To show the advantages of RIS-aided networks, we consider two practical scenarios: Communication with and without RIS. In both cases, we apply the stochastic geometry theory to derive exact probability density function (PDF) and cumulative distribution function (CDF) of the received signal-to-interference-plus-noise ratio. Furthermore, the obtained PDF and CDF are exploited to evaluate important security performance of wireless communication including the secrecy outage probability, the probability of nonzero secrecy capacity, and the average secrecy rate. Monte-Carlo simulations are subsequently conducted to validate the accuracy of our analytical results. Compared with traditional MIMO systems, the RIS-aided system offers better performance in terms of physical layer security. In particular, the security performance is improved significantly by increasing the number of reflecting elements equipped in a RIS. However, adopting RIS equipped with a small number of reflecting elements cannot improve the system performance when the path loss of NLoS is small.

In this paper, we study the optimal wireless streaming of a multi-quality tiled 360 virtual reality (VR) video from a multi-antenna server to multiple single-antenna users in a multiple-input multiple-output (MIMO)-orthogonal frequency division multiple access (OFDMA) system. In the scenario without user transcoding, we jointly optimize beamforming and subcarrier, transmission power, and rate allocation to minimize the total transmission power. This problem is a challenging mixed discrete-continuous optimization problem. We obtain a globally optimal solution for small multicast groups, an asymptotically optimal solution for a large antenna array, and a suboptimal solution for the general case. In the scenario with user transcoding, we jointly optimize the quality level selection, beamforming, and subcarrier, transmission power, and rate allocation to minimize the weighted sum of the average total transmission power and the transcoding power. This problem is a two-timescale mixed discrete-continuous optimization problem, which is even more challenging than the problem for the scenario without user transcoding. We obtain a globally optimal solution for small multicast groups, an asymptotically optimal solution for a large antenna array, and a low-complexity suboptimal solution for the general case. Finally, numerical results demonstrate the significant gains of proposed solutions over the existing solutions.

Orthogonal time frequency space (OTFS) modulation is a recently developed multi-carrier multi-slot transmission scheme for wireless communications in high-mobility environments. In this paper, the error performance of coded OTFS modulation over high-mobility channels is investigated. We start from the study of conditional pairwise-error probability (PEP) of the OTFS scheme, based on which its performance upper bound of the coded OTFS system is derived. Then, we show that the coding improvement for OTFS systems depends on the squared Euclidean distance among codeword pairs and the number of independent resolvable paths of the channel. More importantly, we show that there exists a fundamental trade-off between the coding gain and the diversity gain for OTFS systems, i.e., the diversity gain of OTFS systems improves with the number of resolvable paths, while the coding gain declines. Furthermore, based on our analysis, the impact of channel coding parameters on the performance of the coded OTFS systems is unveiled. The error performance of various coded OTFS systems over high-mobility channels is then evaluated. Simulation results demonstrate a significant performance improvement for OTFS modulation over the conventional orthogonal frequency division multiplexing (OFDM) modulation over high-mobility channels. Analytical results and the effectiveness of the proposed code design are also verified by simulations with the application of both classical and modern codes for OTFS systems.

Tag signal detection is one of the key tasks in ambient backscatter communication (AmBC) systems. However, obtaining perfect channel state information (CSI) is challenging and costly, which makes AmBC systems suffer from a high bit error rate (BER). To eliminate the requirement of channel estimation and to improve the system performance, in this paper, we adopt a deep transfer learning (DTL) approach to implicitly extract the features of channel and directly recover tag symbols. To this end, we develop a DTL detection framework which consists of offline learning, transfer learning, and online detection. Specifically, a DTL-based likelihood ratio test (DTL-LRT) is derived based on the minimum error probability (MEP) criterion. As a realization of the developed framework, we then apply convolutional neural networks (CNN) to intelligently explore the features of the sample covariance matrix, which facilitates the design of a CNN-based algorithm for tag signal detection. Exploiting the powerful capability of CNN in extracting features of data in the matrix formation, the proposed method is able to further improve the system performance. In addition, an asymptotic explicit expression is also derived to characterize the properties of the proposed CNN-based method when the number of samples is sufficiently large. Finally, extensive simulation results demonstrate that the BER performance of the proposed method is comparable to that of the optimal detection method with perfect CSI.

Non-orthogonal multiple access (NOMA) enables massive connectivity and achieves high spectral efficiency. The vast majority of the NOMA literature has adopted the ideal information rate as performance metric assuming perfect successive interference cancellation (SIC) without any error propagation, which, however, may lead to NOMA designs adverse to SIC. In this paper, we take into account imperfect SIC and practical modulation schemes for power-domain NOMA design. To characterize the error propagation, we derive the bit error rates (BERs) of the users for arbitrary-order quadrature amplitude modulation (QAM) schemes. Then, we propose a minimum error probability NOMA (MEP-NOMA) design, minimizing the average BER of the users via power allocation. Considering the complicated error probability expressions of the MEP-NOMA design, we derive lower and upper bounds on the average BER, based on which a simple closed-form power allocation is obtained. We show that the proposed power allocation minimizes both the lower and upper bounds on the average BER for a sufficiently large power budget and provides near-optimal error performance. On this basis, we theoretically prove the superiority of MEP-NOMA over existing OMA and NOMA schemes in terms of error performance. Comprehensive numerical results are provided to verify the accuracy of the error probability analysis of the considered practical NOMA scheme with imperfect SIC and to demonstrate the efficacy of the proposed MEP-NOMA design.

In this paper, we consider the application of intelligent reflecting surface (IRS) in unmanned aerial vehicle (UAV)-based orthogonal frequency division multiple access (OFDMA) communication systems, which exploits both the significant beamforming gain brought by the IRS and the high mobility of UAV for improving the system sum-rate. The joint design of UAV's trajectory, IRS scheduling, and communication resource allocation for the proposed system is formulated as a non-convex optimization problem to maximize the system sum-rate while taking into account the heterogeneous quality-of-service (QoS) requirement of each user. The existence of an IRS introduces both frequency-selectivity and spatial-selectivity in the fading of the composite channel from the UAV to ground users. To facilitate the design, we first derive the expression of the composite channels and propose a parametric approximation approach to establish an upper and a lower bound for the formulated problem. An alternating optimization algorithm is devised to handle the lower bound optimization problem and its performance is compared with the benchmark performance achieved by solving the upper bound problem. Simulation results unveil the small gap between the developed bounds and the promising sum-rate gain achieved by the deployment of an IRS in UAV-based communication systems.

This paper considers the application of reconfigurable intelligent surfaces (RISs) (a.k.a. intelligent reflecting surfaces (IRSs)) to assist multiuser multiple-input multiple-output (MIMO) uplink transmission from several multi-antenna user terminals (UTs) to a multi-antenna base station (BS). For reducing the signaling overhead, only partial channel state information (CSI), including the instantaneous CSI between the RIS and the BS as well as the slowly varying statistical CSI between the UTs and the RIS, is exploited in our investigation. In particular, an optimization framework is proposed for jointly designing the transmit covariance matrices of the UTs and the RIS phase shift matrix to maximize the system global energy efficiency (GEE) with partial CSI. We first obtain closed-form solutions for the eigenvectors of the optimal transmit covariance matrices of the UTs. Then, to facilitate the design of the transmit power allocation matrices and the RIS phase shifts, we derive an asymptotically deterministic equivalent of the objective function with the aid of random matrix theory. We further propose a suboptimal algorithm to tackle the GEE maximization problem with guaranteed convergence, capitalizing on the approaches of alternating optimization, fractional programming, and sequential optimization. Numerical results substantiate the effectiveness of the proposed approach as well as the considerable GEE gains provided by the RIS-assisted transmission scheme over the traditional baselines.

The development of dual-functional radar-communication (DFRC) systems, where vehicle localization and tracking can be combined with vehicular communication, will lead to more efficient future vehicular networks. In this paper, we develop a predictive beamforming scheme in the context of DFRC systems. We consider a system model where the road-side unit estimates and predicts the motion parameters of vehicles based on the echoes of the DFRC signal. Compared to the conventional feedback-based beam tracking approaches, the proposed method can reduce the signaling overhead and improve the accuracy of the angle estimation. To accurately estimate the motion parameters of vehicles in real-time, we propose a novel message passing algorithm based on factor graph, which yields a near optimal performance achieved by the maximum a posteriori estimation. The beamformers are then designed based on the predicted angles for establishing the communication links. With the employment of appropriate approximations, all messages on the factor graph can be derived in a closed-form, thus reduce the complexity. Simulation results show that the proposed DFRC based beamforming scheme is superior to the feedback-based approach in terms of both estimation and communication performance. Moreover, the proposed message passing algorithm achieves a similar performance of the high-complexity particle filtering-based methods.

This paper investigates multiuser multi-input single-output (MISO) downlink communications assisted by a self-sustainable intelligent reflection surface (IRS), which can harvest power from the received signals. We study the joint design of the beamformer at an access point (AP) and the phase shifts and the power harvesting schedule at an IRS for maximizing the system sum-rate. The design is formulated as a non-convex optimization problem taking into account the capability of IRS elements to harvest wireless power for realizing self-sustainability. Subsequently, we propose a computationally-efficient alternating algorithm to obtain a suboptimal solution to the design problem. Our simulation results unveil that: 1) there is a non-trivial trade-off between the system sum-rate and self-sustainability in IRS-assisted systems; 2) the performance gain achieved by the proposed scheme is improved with an increasing number of IRS elements; 3) an IRS equipped with small bit-resolution discrete phase shifters is sufficient to achieve a considerable system sumrate of an ideal case with continuous phase shifts.

In this paper, by taking into account the requirements of information secrecy and transmission resiliency, we present a comprehensive scheme enabling secure device-to-device (D2D) networks, where a single-antenna transmitter communicates with a half-duplex single-antenna receiver in the presence of a passive eavesdropper and an adversary jammer. Motivated by physical layer security techniques, artificial noise injection scheme is proposed to ensure communication secrecy. To improve the resiliency against jamming attack, the D2D nodes utilize the frequency hopping technique. Under this system model, we examine the achievable ergodic secrecy rate (ESR) by deriving a new closed-form expression. Furthermore, the optimal power allocation between the artificial noise and data signal is studied for maximizing the ESR. Numerical examples and discussions are provided to depict the efficiency of our proposed scheme compared with the state-of-the-arts.

Existing tag signal detection algorithms inevitably suffer from a high bit error rate (BER) due to the difficulties in estimating the channel state information (CSI). To eliminate the requirement of channel estimation and to improve the system performance, in this paper, we adopt a deep transfer learning (DTL) approach to implicitly extract the features of communication channel and directly recover tag symbols. Inspired by the powerful capability of convolutional neural networks (CNN) in exploring the features of data in a matrix form, we design a novel covariance matrix aware neural network (CMNet)-based detection scheme to facilitate DTL for tag signal detection, which consists of offline learning, transfer learning, and online detection. Specifically, a CMNet-based likelihood ratio test (CMNet-LRT) is derived based on the minimum error probability (MEP) criterion. Taking advantage of the outstanding performance of DTL in transferring knowledge with only a few training data, the proposed scheme can adaptively fine-tune the detector for different channel environments to further improve the detection performance. Finally, extensive simulation results demonstrate that the BER performance of the proposed method is comparable to that of the optimal detection method with perfect CSI.

This paper designs a cooperative activity detection framework for massive grant-free random access in the sixth-generation (6G) cell-free wireless networks based on the covariance of the received signals at the access points (APs). In particular, multiple APs cooperatively detect the device activity by only exchanging the low-dimensional intermediate local information with their neighbors. The cooperative activity detection problem is non-smooth and the unknown variables are coupled with each other for which conventional approaches are inapplicable. Therefore, this paper proposes a covariance-based algorithm by exploiting the sparsity-promoting and similarity-promoting terms of the device state vectors among neighboring APs. An approximate splitting approach is proposed based on the proximal gradient method for solving the formulated problem. Simulation results show that the proposed algorithm is efficient for large-scale activity detection problems while requires shorter pilot sequences compared with the state-of-art algorithms in achieving the same system performance.

This paper studies covert millimeter-wave (mmWave) communications, where a multi-antenna transmitter (Alice) sends information signals to a full-duplex receiver (Bob) covertly, in the presence of a warden (Willie). For covering the communication by Alice, Bob operates in full-duplex mode which generates jamming signals with a transmit power varying across different time slots. Assuming that Willie adopts a radiometer as its detector, we first derive the optimal detection threshold for Willie. Next, we jointly design the analog beamforming at Alice and the analog jamming at Bob for maximizing the covert rate taking into account the use of optimal detecting at Willie and the communication outage probability experienced by Bob. Although the joint design is nonconvex that is challenging to solve directly, a successive convex approximation algorithm-based algorithm is developed to address the design problem. Simulation results validate the efficiency of the proposed algorithm, compared to some baseline scheme.

In this paper, we propose the use of intelligent reflecting surface (IRS) in unmanned aerial vehicle (UAV)- based orthogonal frequency division multiple access (OFDMA) communication systems. The proposed scheme exploits both the rich beamforming gain brought by the IRS and the high mobility of UAV for improving the system sum-rate. The joint design of UAV's trajectory, IRS scheduling, and communication resource allocation for the proposed system is formulated as a non-convex optimization problem to maximize the system sum-rate. The existence of an IRS introduces both frequency selectivity and spatial-selectivity in the fading of the composite channel from the UAV to ground users. To facilitate the design, we first derive the expression of the composite channel gain and propose a parametric approximation approach to establish a lower bound for the formulated problem. An alternating optimization algorithm is devised to handle the lower bound optimization problem. Simulation results unveil the promising sum-rate gain achieved by the deployment of an IRS in UAV-based communication systems.

We study the tradeoff between energy efficiency (EE) and spectral efficiency (SE) in multiuser multiple-input multiple-output (MIMO) uplink communications aided by a reconfigurable intelligent surface (RIS) equipped with discrete phase shifters. For reducing the required signaling overhead and energy consumption, our design is based on the partial channel state information (CSI), including the statistical CSI between the RIS and user terminals (UTs) and the instantaneous CSI between the RIS and the base station. To investigate the EE-SE tradeoff, we develop a framework for the joint optimization of UTs' transmit precoding and RIS reflective beamforming to maximize a metric called resource efficiency. Based on the closed-form solutions of all UTs' optimal transmit subspace and an asymptotic objective expression, an optimization framework is proposed via exploiting the quadratic transformation, the homotopy, accelerated projected gradient, and majorization-minimization methods. Numerical results illustrate the effectiveness of our optimization framework for the considered RIS-aid communications.

Intelligent reflecting surfaces (IRSs) are regarded as key enablers of next-generation wireless communications, due to their capability of customizing the wireless propagation environment. In this paper, we investigate power-efficient resource allocation for IRS-assisted multiuser multiple-input single-output (MISO) systems. To minimize the transmit power, both the beamforming vectors at the access point (AP) and phase shifts at the IRS are jointly optimized while taking into account the minimum required quality-of-service (QoS) of the users. To tackle the non-convexity of the formulated optimization problem, an inner approximation (IA) algorithm is developed. Unlike existing designs, which cannot guarantee local optimality, the proposed algorithm is guaranteed to converge to a Karush-Kuhn-Tucker (KKT) solution. Our simulation results show the effectiveness of the proposed algorithm compared to baseline schemes and reveal that deploying IRSs is more promising than leveraging multiple antennas at the AP in terms of energy efficiency.

Location awareness becomes an essential requirement for numerous applications and services in the future wireless communications. This paper addresses the problem of joint localization and synchronization in a network with cooperative nodes. The focus of this work is on the design of a low-complexity yet near-optimal message-passing implementations. To avoid the high-complexity of applying particle filtering-based approaches, we suitably augment the factor graph by introducing auxiliary variables. Then we propose a hybrid method that combines belief propagation (BP) and mean field (MF) message passing, which are used for message updating in the synchronization and localization parts of the factor graph, respectively. As a result, all messages on factor graph can be represented in parametric forms such that the proposed algorithm features a significantly low complexity while achieving near-optimal positioning performance.

In this paper, we formulate two multi-objective optimization problems (MOOPs) in orthogonal frequency-division multiple access (OFDMA)-based in-band full-duplex (IBFD) wireless communications. The aim of this study is to exploit the performance trade-off between uplink and downlink where a wireless radio simultaneously transmits and receives in the same frequency. We consider maximizing the system throughput as the first MOOP and minimizing the system aggregate power consumption as the second MOOP between uplink and downlink, while taking into account the impact of self-interference (SI) and quality of service provisioning. We study the throughput and the transmit power trade-off between uplink and downlink via solving these two problems. Each MOOP is a nonconvex mixed integer non-linear programming (MINLP) which is generally intractable. In order to circumvent this difficulty, a penalty function is introduced to reformulate the problem into a mathematically tractable form. Subsequently, each MOOP is transformed into a single-objective optimization problem (SOOP) via the weighted Tchebycheff method which is addressed by majorization-minimization (MM) approach. Simulation results demonstrate an interesting trade-off between the considered competing objectives.

This paper considers the application of reconfigurable intelligent surfaces (RISs) to assist multiuser multiple-input multiple-output multiple access channel (MIMO-MAC) systems. In contrast to most existing works on RIS-assisted systems assuming the availability of full channel state information (CSI), only partial CSI is required in our investigation, including the instantaneous CSI of the channel from a RIS to a base station and the statistical CSI of the channels from user terminals (UTs) to the RIS. We investigate the joint design of both the transmit covariance matrices of the UTs and the RIS phase shift matrix under the system global energy efficiency (GEE) maximization criterion. To maximize the GEE, we first obtain closed-form solutions for the eigenvectors of the optimal transmit covariance matrices of the UTs. Then, we derive an asymptotic expression of the objective function with the aid of random matrix theory to reduce the computational cost. We further propose a low-complexity algorithm to tackle the GEE maximization problem with guaranteed convergence, capitalizing on the approaches of alternating optimization, fractional programming, and sequential optimization. Numerical results substantiate the effectiveness of the proposed approach as well as the GEE performance gains provided by RIS-assisted MIMO-MAC systems.

Both faster than Nyquist (FTN) signaling and non-orthogonal multiple access (NOMA) are promising next generation wireless communications techniques as a benefit of their capability of improving the system's spectral efficiency. This paper considers an uplink system that combines the advantages of FTN and NOMA. Consequently, an improved spectral efficiency is achieved by deliberately introducing both inter-symbol interference (ISI) and inter-user interference (IUI). More specifically, we propose a grant-free transmission scheme to reduce the signaling overhead and transmission latency of the considered NOMA system. To distinguish the active and inactive users, we develop a novel message passing receiver that jointly estimates the channel state, detects the user activity, and performs decoding. We conclude by quantifying the significant spectral efficiency gain achieved by our amalgamated FTN-NOMA scheme compared to the orthogonal transmission system, which is up to 87.5%.

This paper considers non-orthogonal multiple access (NOMA) based cell-free massive multiple-input multiple-output (mMIMO) systems over spatially correlated Rician fading channels. Closed-form downlink achievable sum-rate expression is derived by taking into account spatial correlation among multi-antenna access points, inter-cluster interference, intra-cluster pilot contamination, and imperfect successive interference cancellation (SIC). In particular, we propose an intra-cluster power allocation design for improving the system performance. Furthermore, we investigate the downlink performance with both minimum mean-squared error (MMSE) and element-wise MMSE channel estimation. It is interesting to find out that the correlation magnitude has a negligible effect on the system sum-rate in spatially correlated Rician fading channels. The numerical results validate the correctness of the presented results and confirm the effectiveness of the proposed power allocation.

Cognitive radio is a promising technology to improve spectral efficiency. However, communication security of a secondary network is limited by its transmit power and channel fading. In order to tackle this issue, by exploiting the high flexibility and the possibility of establishing line-of-sight links, a cognitive unmanned aerial vehicle (UAV) communication network is studied. The average secrecy rate of the secondary network is maximized by robustly optimizing the UAVs trajectory and transmit power. Our formulated problem takes into account practical imperfect location estimation. To solve the non-convex problem, an iterative suboptimal algorithm based on the Bernstein-type inequalities is presented. Our simulation results demonstrate that the proposed scheme can improve the secure communication performance significantly compared to a benchmark scheme based on fixed trajectory.

In this paper, we investigate the joint resource allocation and antenna selection algorithm design for uplink orthogonal frequency division multiple access (OFDMA) communication system. We propose a multi-objective optimization framework to strike a balance between spectral efficiency (SE) and energy efficiency (EE). The resource allocation design is formulated as a multi-objective optimization problem (MOOP), where the conflicting objective functions are linearly combined into a single objective function employing the weighted sum method. In order to develop an efficient solution, the majorization minimization (MM) approach is proposed where a surrogate function serves as a lower bound of the objective function. Then an iterative suboptimal algorithm is proposed to maximize the approximate objective function. Numerical results unveil an interesting tradeoff between the considered conflicting system design objectives and reveal the improved EE and SE facilitated by the proposed transmit antenna selection in OFDMA systems.

In this article, we apply the large intelligent reflecting surface (IRS) technique in beyond fifth-generation (B5G) cellular Internet of Things (IoT) to satisfy the requirements of massive connectivity, low power, and wide coverage. First, we design a framework for the large IRS-aided B5G cellular IoT, including channel estimation, uplink data transmission, and downlink data transmission. Then, we analyze the performance of the proposed framework, and reveal the impacts of key parameters of the large IRS on the spectral efficiency. Next, we propose a low-complexity time-length allocation algorithm to minimize the total energy consumption of B5G cellular IoT. Finally, extensive simulation results validate the accuracy of the derived theoretical expressions and the effectiveness of the proposed algorithm.

In this paper, intelligent reflecting surfaces (IRSs) are employed to enhance the physical layer security in a challenging radio environment. In particular, a multi-antenna access point (AP) has to serve multiple single-antenna legitimate users, which do not have line-of-sight communication links, in the presence of multiple multi-antenna potential eavesdroppers whose channel state information (CSI) is not perfectly known. Artificial noise (AN) is transmitted from the AP to deliberately impair the eavesdropping channels for security provisioning. We investigate the joint design of the beamformers and AN covariance matrix at the AP and the phase shifters at the IRSs for maximization of the system sum-rate while limiting the maximum information leakage to the potential eavesdroppers. To this end, we formulate a robust non-convex optimization problem taking into account the impact of the imperfect CSI of the eavesdropping channels. To address the non-convexity of the optimization problem, an efficient algorithm is developed by capitalizing on alternating optimization, a penalty-based approach, successive convex approximation, and semidefinite relaxation. Simulation results show that IRSs can significantly improve the system secrecy performance compared to conventional architectures without IRS. Furthermore, our results unveil that, for physical layer security, uniformly distributing the reflecting elements among multiple IRSs is preferable over deploying them at a single IRS.

Recently, the first version of the fifth-generation (5G) new radio (NR) standard with massive multiple-input multiple-output (MIMO) has been finished by the 3rd Generation Partnership Project (3GPP), with initial deployments occurring in 2018. Despite the major advances in 5G, there are still many challenges remaining. 6G and beyond will require even higher data rates, lower latencies, better energy efficiency, and improved robustness.Multiple antenna technologies,which have played important roles in nearly all recent wireless standards, will be key to addressing these challenges. MIMO research continues to evolve, and new MIMO research topics such as enhanced massive MIMO techniques and array architectures hold much potential for 6G and beyond. Cell-free massive MIMO utilize a large number of distributed access points (APs) that jointly serve users in a coordinated fashion, using only local channel state information at each AP. While the performance of cell-free massive MIMO can be analyzed using a similar methodology as in cellular massive MIMO, the fundamental limits, signal processing, and resource allocation are substantially different. In order to reduce the hardware cost and energy consumption in millimeter wave (mmWave) massive MIMO systems, beamspace MIMO has been proposed to significantly reduce the number of required radio-frequency (RF) chains by using lens antenna arrays or phase shifters. Alternatively, the intelligent reflecting surface (IRS) concept involves electromagnetically controllable surfaces that can be integrated into large-scale infrastructure such as building walls, airports, and stadiums. There are active and partially passive forms of large intelligent surface (LIS), and variants with either large antenna spacing or continuous aperture. There are also some substantial differences between the new multiple antenna technologies and traditional MIMO systems, such as transceiver design and propagation models. This special issue aims to highlight recent research on multiple antenna technologies.

Multiple antenna technologies have attracted much research interest for several decades and have gradually made their way into mainstream communication systems. Two main benefits are adaptive beamforming gains and spatial multiplexing, leading to high data rates per user and per cell, especially when large antenna arrays are adopted. Since multiple antenna technology has become a key component of the fifth-generation (5G) networks, it is time for the research community to look for new multiple antenna technologies to meet the immensely higher data rate, reliability, and traffic demands in the beyond 5G era. Radically new approaches are required to achieve orders-of-magnitude improvements in these metrics. There will be large technical challenges, many of which are yet to be identified. In this paper, we survey three new multiple antenna technologies that can play key roles in beyond 5G networks: cell-free massive MIMO, beamspace massive MIMO, and intelligent reflecting surfaces. For each of these technologies, we present the fundamental motivation, key characteristics, recent technical progresses, and provide our perspectives for future research directions. The paper is not meant to be a survey/tutorial of a mature subject, but rather serve as a catalyst to encourage more research and experiments in these multiple antenna technologies.

Recently, the first version of the fifth-generation (5G) new radio (NR) standard with massive multiple-input multiple-output (MIMO) has been finished by the 3rd Generation Partnership Project (3GPP), with initial deployments occurring in 2018. Despite the major advances in 5G, there are still many challenges remaining. 6G and beyond will require even higher data rates, lower latencies, better energy efficiency, and improved robustness.Multiple antenna technologies,which have played important roles in nearly all recent wireless standards, will be key to addressing these challenges. MIMO research continues to evolve, and new MIMO research topics such as enhanced massive MIMO techniques and array architectures hold much potential for 6G and beyond. Cell-free massive MIMO utilize a large number of distributed access points (APs) that jointly serve users in a coordinated fashion, using only local channel state information at each AP. While the performance of cell-free massive MIMO can be analyzed using a similar methodology as in cellular massive MIMO, the fundamental limits, signal processing, and resource allocation are substantially different. In order to reduce the hardware cost and energy consumption in millimeter wave (mmWave) massive MIMO systems, beamspace MIMO has been proposed to significantly reduce the number of required radio-frequency (RF) chains by using lens antenna arrays or phase shifters. Alternatively, the intelligent reflecting surface (IRS) concept involves electromagnetically controllable surfaces that can be integrated into large-scale infrastructure such as building walls, airports, and stadiums. There are active and partially passive forms of large intelligent surface (LIS), and variants with either large antenna spacing or continuous aperture. There are also some substantial differences between the new multiple antenna technologies and traditional MIMO systems, such as transceiver design and propagation models. This special issue aims to highlight recent research on multiple antenna technologies.

In this paper, we study the trajectory and resource allocation design for downlink energy-efficient secure unmanned aerial vehicle (UAV) communication systems, where an information UAV assisted by a multi-antenna jammer UAV serves multiple ground users in the existence of multiple ground eavesdroppers. The resource allocation strategy and the trajectory of the information UAV, and the jamming policy of the jammer UAV are jointly optimized for maximizing the system energy efficiency. The joint design is formulated as a non-convex optimization problem taking into account the quality of service (QoS) requirement, the security constraint, and the imperfect channel state information (CSI) of the eavesdroppers. The formulated problem is generally intractable. As a compromise approach, the problem is divided into two subproblems which facilitates the design of a low-complexity suboptimal algorithm based on alternating optimization approach. Simulation results illustrate that the proposed algorithm converges within a small number of iterations and demonstrate some interesting insights: (1) the introduction of a jammer UAV facilitates a highly flexible trajectory design of the information UAV which is critical to improving the system energy efficiency; (2) by exploiting the spatial degrees of freedom brought by the multi-antenna jammer UAV, our proposed design can focus the artificial noise on eavesdroppers offering a strong security mean to the system.

In this paper, we study the resource allocation and trajectory design for secure unmanned aerial vehicle (UAV)-enabled communication systems, where multiple multi-purpose UAV base stations are dispatched to provide secure communications to multiple legitimate ground users (GUs) in the existence of multiple eavesdroppers (Eves). Specifically, by leveraging orthogonal frequency division multiple access (OFDMA), active UAV base stations can communicate to their desired ground users via the assigned subcarriers while idle UAV base stations can serve as jammer simultaneously for communication security provisioning. To achieve fairness in secure communication, we maximize the average minimum secrecy rate per user by jointly optimizing the communication/jamming subcarrier allocation policy and the trajectory of UAVs, while taking into account the constraints on the minimum safety distance among multiple UAVs, the maximum cruising speed, the initial/final locations, and the existence of cylindrical no-fly zones (NFZs). The design is formulated as a mixed integer non-convex optimization problem which is generally intractable. Subsequently, a computationally-efficient iterative algorithm is proposed to obtain a suboptimal solution. Simulation results illustrate that the performance of the proposed iterative algorithm can significantly improve the average minimum secrecy rate compared to various baseline schemes.

Visible light communication (VLC) is a promising technology for the next generation wireless communication systems due to its high spectral efficiency and energy efficiency. In this article, a secure multiple-input single-output (MISO) VLC network is studied, where simultaneous lightwave information and power transfer (SLIPT) is exploited to support multiple energy-limited devices tacking into account a practical non-linear energy harvesting model. The transmit power minimization and the minimum secrecy rate maximization problems are formulated under both perfect and imperfect channel state information, respectively. To further improve the user connectivity, those problems are also investigated in MISO-VLC networks with non-orthogonal multiple access (NOMA). To tackle these challenging non-convex problems, semidefinite program relaxation and $-Procedure are exploited. It is proved that optimal beamforming schemes can be obtained for the considered two types problems in MISO-VLC SLIPT networks, while a sub-optimal solution can be obtained for the transmit power minimization problem in those networks with NOMA. It is found that there is a non-trivial trade-off between the average harvested power and maximum-minimum secrecy rate. Moreover, it is shown that the performance achieved with NOMA outperforms that of conventional orthogonal multiple access in MISO-VLC SLIPT networks, despite the existence of imperfect channel state information.

Spectrally efficient frequency division multiplexing (SEFDM) relying on index modulation (IM) has emerged as a promising multicarrier technique. In this paper, we develop a joint channel estimation and equalization method based on factor graphs for SEFDM-IM signaling over frequency-selective fading channels. By approximating the interference in the frequency domain, we reformulate the problem to obey a linear state-space model and construct a multi-layer factor graph. To support a reconfigurable architecture, non-orthogonal demodulation is adopted and the colored noise encountered is approximated by a complex auto-regressive (CAR) model. For deriving a low-complexity parametric Gaussian message passing (GMP)-based method, we exploit an expectation propagation (EP)-based technique for approximating the discrete a posteriori distributions of the transmitted symbols in a Gaussian form. To further simplify the result, variational message passing (VMP) is applied to an equivalent soft node to obtain a Gaussian form. Moreover, we also derive the Cramér-Rao lower bound (CRLB) in closed-form. The overall complexity only grows linearly with the number of subcarriers and logarithmically with the length of the channel's memory. Compared to its Nyquist signaling based counterpart, SEFDM-IM signaling relying on the proposed algorithm exhibits up to 25% higher bandwidth efficiency without any bit error rate (BER) performance degradation.

In this paper, we study information-theoretic limits for simultaneous wireless information and power transfer (SWIPT) systems employing practical nonlinear radio frequency (RF) energy harvesting (EH) receivers (Rxs). In particular, we consider a SWIPT system with one transmitter that broadcasts a common signal to an information decoding (ID) Rx and multiple EH Rxs. Owing to the nonlinearity of the EH Rxs' circuitry, the efficiency of wireless power transfer depends on the waveform of the transmitted signal. We aim to answer the following fundamental question: What is the optimal input distribution of the transmit signal waveform that maximizes the information transfer rate at the ID Rx conditioned on individual minimum required direct-current (DC) powers to be harvested at the EH Rxs? Specifically, we study the conditional capacity problem of a SWIPT system impaired by additive white Gaussian noise subject to average-power (AP) and peak-power (PP) constraints at the transmitter and nonlinear EH constraints at the EH Rxs. To this end, we develop a novel nonlinear EH model that captures the saturation of the harvested DC power by taking into account not only the forward current of the rectifying diode but also the reverse breakdown current. Then, we derive a novel semi-closed-form expression for the harvested DC power, which simplifies to closed form for low input RF powers. The derived analytical expressions are shown to closely match circuit simulation results. We solve the conditional capacity problem for real- and complex-valued signalling and prove that the optimal input distribution that maximizes the rate-energy (R-E) region is unique and discrete with a finite number of mass points. Furthermore, we show that, for the considered nonlinear EH model and a given AP constraint, the boundary of the R-E region saturates for high PP constraints due to the saturation of the harvested DC power for high input RF powers. In addition, we devise a suboptimal input distribution whose R-E tradeoff performance is close to optimal. All theoretical findings are verified by numerical evaluations.

To analyze the achievable performance of antenna selection (AS) in practical multi-antenna systems, this paper studies the receive antenna selection (RAS) in single-input multiple-output (AS-SIMO) systems under discrete inputs. We first propose an approximate expression to evaluate the instantaneous mutual information (MI) of M-ary quadrature amplitude modulation (M-QAM) signaling over additive white Gaussian noise (AWGN) channels. Then, by exploiting this approximate formula, we develop a closed-form formula for the ergodic MI in AS-SIMO systems with M-QAM signaling. Additionally, we also analyze the asymptotic MI for a large number of receive antennas Nr. This asymptotic analysis suggests that the scaling rate of the MI with Nr becomes zero rate in contrast to the double logarithmic rate under Gaussian inputs. Besides, our result is also extended to discuss the mutual information of multiple-input multiple-output (MIMO) systems having discrete inputs with receive antenna selection, and an upper bound for the MI is derived. Finally, the derived result is applied to analyze several performance measures of the discrete inputs driven ASSIMO systems. Specifically, it is first used to discuss the relationship between the ergodic MI and the number of active antennas. Our investigation shows that this relationship follows Pareto principle, i.e., 80% of the MI of full-antenna selection can be achieved via 20% of the total antennas. Then, our proposed approximation is employed to analytically study the effective MI which takes channel estimation (CE) into consideration, indicating that CE is a main limit of large-scale systems. Moreover, the energy efficiency (EE) is explored on the basis of our results, and we find there exists an optimal number of active antennas to maximize the energy efficiency. In addition to theoretical derivations, all the analytical results are validated by numerical simulations.

In this paper, we consider an energy-constrained unmanned aerial vehicle (UAV)-enabled mobile relay assisted secure communication system in the presence of a legitimate source-destination pair and multiple eavesdroppers with imperfect locations. The energy-constrained UAV employs the power splitting (PS) scheme to simultaneously receive information and harvest energy from the source, and then exploits the time switching (TS) protocol to perform information relaying. Furthermore, we consider a full-duplex destination node which can simultaneously receive confidential signals from the UAV and cooperatively transmit artificial noise (AN) signals to confuse malicious eavesdroppers. To further enhance the reliability and security of this system, we formulate a worst case secrecy rate maximization problem, which jointly optimizes the position of the UAV, the AN transmit power, as well as the PS and TS ratios. The formulated problem is non-convex and generally intractable. In order to circumvent the non-convexity, we decouple the original optimization problem into three subproblems; this facilitates the design of a suboptimal iterative algorithm. In each iteration, we propose a multi-dimensional search and numerical method to handle the subproblem. Numerical simulation results are provided to demonstrate the effectiveness and superior performance of the proposed joint design versus the conventional schemes in the literature.

In this paper, we investigate and reveal the ergodic sum-rate gain (ESG) of non-orthogonal multiple access (NOMA) over orthogonal multiple access (OMA) in uplink cellular communication systems. A base station equipped with a single-antenna, with multiple antennas, and with massive antenna arrays is considered both in single-cell and multi-cell deployments. In particular, in single-antenna systems, we identify two types of gains brought about by NOMA: 1) a large-scale near-far gain arising from the distance discrepancy between the base station and users; 2) a small-scale fading gain originating from the multipath channel fading. Furthermore, we reveal that the large-scale near-far gain increases with the normalized cell size, while the small-scale fading gain is a constant, given by γ = 0.57721 nat/s/Hz, in Rayleigh fading channels. When extending single-antenna NOMA to M-antenna NOMA, we prove that both the large-scale near-far gain and small-scale fading gain achieved by single-antenna NOMA can be increased by a factor of M for a large number of users. Moreover, given a massive antenna array at the base station and considering a fixed ratio between the number of antennas, M, and the number of users, K, the ESG of NOMA over OMA increases linearly with both M and K. We then further extend the analysis to a multi-cell scenario. Compared to the single-cell case, the ESG in multi-cell systems degrades as NOMA faces more severe inter-cell interference due to the non-orthogonal transmissions. Besides, we unveil that a large cell size is always beneficial to the ergodic sum-rate performance of NOMA in both single-cell and multi-cell systems. Numerical results verify the accuracy of the analytical results derived and confirm the insights revealed about the ESG of NOMA over OMA in different scenarios.

In this paper, we investigate robust resource allocation algorithm design for multiuser downlink multiple-input single-output (MISO) unmanned aerial vehicle (UAV) communication systems, where we account for the various uncertainties that are unavoidable in such systems and, if left unattended, may severely degrade system performance. We jointly optimize the two-dimensional (2-D) trajectory and the transmit beamforming vector of the UAV for minimization of the total power consumption. The algorithm design is formulated as a non-convex optimization problem taking into account the imperfect knowledge of the angle of departure (AoD) caused by UAV jittering, user location uncertainty, wind speed uncertainty, and polygonal no-fly zones (NFZs). Despite the non-convexity of the optimization problem, we solve it optimally by employing monotonic optimization theory and semidefinite programming relaxation which yields the optimal 2-D trajectory and beamforming policy. Since the developed optimal resource allocation algorithm entails a high computational complexity, we also propose a suboptimal iterative low-complexity scheme based on successive convex approximation to strike a balance between optimality and computational complexity. Our simulation results reveal not only the significant power savings enabled by the proposed algorithms compared to two baseline schemes, but also confirm their robustness with respect to UAV jittering, wind speed uncertainty, and user location uncertainty. Moreover, our results unveil that the joint presence of wind speed uncertainty and NFZs has a considerable impact on the UAV trajectory. Nevertheless, by counteracting the wind speed uncertainty with the proposed robust design, we can simultaneously minimize the total UAV power consumption and ensure a secure trajectory that does not trespass any NFZ.

In this paper, we investigate secure communication over sparse millimeter-wave (mm-Wave) massive multiple-input multiple-output (MIMO) channels by exploiting the spatial sparsity of legitimate user's channel. We propose a secure communication scheme in which information data is precoded onto dominant angle components of the sparse channel through a limited number of radio-frequency (RF) chains, while artificial noise (AN) is broadcast over the remaining nondominant angles interfering only with the eavesdropper with a high probability. It is shown that the channel sparsity plays a fundamental role analogous to secret keys in achieving secure communication. Hence, by defining two statistical measures of the channel sparsity, we analytically characterize its impact on secrecy rate. In particular, a substantial improvement on secrecy rate can be obtained by the proposed scheme due to the uncertainty, i.e., “entropy”, introduced by the channel sparsity which is unknown to the eavesdropper. It is revealed that sparsity in the power domain can always contribute to the secrecy rate. In contrast, in the angle domain, there exists an optimal level of sparsity that maximizes the secrecy rate. The effectiveness of the proposed scheme and derived results are verified by numerical simulations.

In this article, we investigate the resource allocation design for intelligent reflecting surface (IRS)-assisted full-duplex (FD) cognitive radio systems. In particular, a secondary network employs an FD base station (BS) for serving multiple half-duplex downlink (DL) and uplink (UL) users simultaneously. An IRS is deployed to enhance the performance of the secondary network while helping to mitigate the interference caused to the primary users (PUs). The DL transmit beamforming vectors and the UL receive beamforming vectors at the FD BS, the transmit power of the UL users, and the phase shift matrix at the IRS are jointly optimized for maximization of the total spectral efficiency of the secondary system. The design task is formulated as a non-convex optimization problem taking into account the imperfect knowledge of the PUs' channel state information (CSI) and their maximum interference tolerance. Since the maximum interference tolerance constraint is intractable, we apply a safe approximation to transform it into a convex constraint. To efficiently handle the resulting approximated optimization problem, which is still non-convex, we develop an iterative block coordinate descent (BCD)-based algorithm. This algorithm exploits semidefinite relaxation, a penalty method, and successive convex approximation and is guaranteed to converge to a stationary point of the approximated optimization problem. Our simulation results do not only reveal that the proposed scheme yields a substantially higher system spectral efficiency for the secondary system than several baseline schemes, but also confirm its robustness against CSI uncertainty. Besides, our results illustrate the tremendous potential of IRS for managing the various types of interference arising in FD cognitive radio networks.

Given the requirements of increased data rate and massive connectivity in the Internet-of-things (IoT) applications of the fifth-generation communication systems (5G), non-orthogonal multiple access (NOMA) was shown to be capable of supporting more users than OMA. As a further potential enhancement, the faster-than-Nyquist (FTN) signaling is also capable of increasing the symbol rate. Since NOMA and FTN signaling impose non-orthogonalities from different perspectives, it is possible to achieve further increased spectral efficiency by exploiting both. Hence we investigate the FTN-NOMA uplink in the context of random access. Although random access schemes reduce the signaling overheads as well as latency, they require the base station to identify active users before performing data detection. As both inter-symbol and inter-user interferences exist, performing optimal detection requires a prohibitively high complexity. Moreover, in typical mobile communication environments, the channel envelope of users fluctuates violently, which imposes challenges on the receiver design. To tackle this problem, we propose a joint user activity tracking and data detection algorithm based on the factor graph framework, which relies on a sophisticated amalgam of expectation maximization (EM) and hybrid message passing algorithms. The complexity of the algorithm advocated only increases linearly with the number of active users. Our simulation results show that the proposed algorithm is effective in tracking user activity and detecting data symbols in dynamic random access systems.

In this paper, we present a comprehensive framework for the performance analysis of dual-hop relaying communications with variable gain amplify-and-forward (AF) relays and operating in the presence of both multipath fading and shadowing, modeled by the Fisher-Snedecor F-distribution. Novel closed-form expressions for the probability density function (PDF) and the cumulative distribution function (CDF) of the end-to-end signal-to-noise ratio (SNR) of the considered system subject to hardware impairments are first derived. Single-integral expressions for the numerical evaluation of the n-th moment of the end-to-end SNR, the outage probability (OP), the ergodic capacity under different adaptive transmission schemes, the effective capacity and the average bit error rate (ABER) of M-ary modulation schemes are further presented. The proposed analytical expressions are valid for most of the well-known fading distributions, provided that the moment generating function (MGF) of the inverse SNR of each hop is readily available. For the special case of ideal hardware, it is shown that the above performance metrics can be expressed in closed-form. It is worth pointing out that the proposed analysis is valid even when the destination node is equipped with multiple antennas and maximal ratio combining (MRC) is employed. The correctness of the proposed mathematical analysis is validated through extensive numerically evaluated results accompanied with Monte-Carlo simulations.

Cognitive radio is a promising technology to improve spectral efficiency. However, the secure performance of a secondary network achieved by using physical layer security techniques is limited by its transmit power and channel fading. In order to tackle this issue, a cognitive unmanned aerial vehicle (UAV) communication network is studied by exploiting the high flexibility of a UAV and the possibility of establishing line-of-sight links. The average secrecy rate of the secondary network is maximized by robustly optimizing the UAV's trajectory and transmit power. Our problem formulation takes into account two practical inaccurate location estimation cases, namely, the worst case and the outage-constrained case. In order to solve those challenging non-convex problems, an iterative algorithm based on S-Procedure is proposed for the worst case while an iterative algorithm based on Bernstein-type inequalities is proposed for the outage-constrained case. The proposed algorithms can obtain effective suboptimal solutions of the corresponding problems. Our simulation results demonstrate that the algorithm under the outage-constrained case can achieve a higher average secrecy rate with a low computational complexity compared to that of the algorithm under the worst case. Moreover, the proposed schemes can improve the secure communication performance significantly compared to other benchmark schemes.

In this paper, we consider max-min fairness for a downlink two-user multi-input single-output (MISO) system with imperfect channel state information available at transmitter (CSIT) taking into account the total power constraint and the physical layer security. Considering the worst-case channel uncertainty for a potential eavesdropper (PE), we study the robust secure beamforming algorithm design which maximizes the minimum achieved worst-case secrecy rate among single-antenna legitimate users. In contrast to existing schemes adopted in the literatures, we propose a rather unorthodox rate splitting (RS) scheme which advocates the dual use of a common message serving both as a desired message and artificial noise (AN) for legitimate users and the PE, respectively. The algorithm design is formulated as a non-convex optimization problem which is generally intractable. As a compromise approach, we apply the successive convex approximation (SCA) method which facilitates the design of a low-complexity suboptimal iterative algorithm. In each iteration, a rank-constrained semi-definite program (SDP) is solved optimally by SDP relaxation (SDR). Simulation results demonstrate that our proposed robust secure beamforming scheme in the MISO-RS secure transmission system outperforms that of the non-robust counterpart. Moreover, our results also unveil that the proposed RS scheme can achieve a superior performance compared to the existing non-orthogonal multiple access (NOMA) schemes and the traditional scheme.

This paper aims at investigating the problem of energy efficiency (EE) maximization for uplink multi-cell networks via a joint design of sub-channel assignment, power control, and antenna selection. We study the problem under two practical scenarios. In the first scenario, known as conventional antenna selection (CAS), there is only one radio frequency (RF) chain available at the mobile user and all the sub-channels for each user can be assigned to one of the antennas. For the second scenario, known as generalized antenna selection (GAS), the number of RF chains is equal to the number of antennas and the messages of each user can transmit over its assigned sub-channels via different antennas. The resource allocation design is formulated as a multi-objective optimization problem (MOOP) and then converted into a single objective optimization problem (SOOP) via the weighted Tchebycheff method. The considered problem is a mixed integer nonlinear programming (MINLP) which is generally intractable. To address this problem, a penalty function is introduced to handle the binary variable constraints. In order to obtain a computationally efficient suboptimal solution, the majorization minimization (MM) approach is proposed where a surrogate function serves as the lower bound of the objective function. Furthermore, we propose another low-complexity practical algorithm to further reduce the computational cost. Simulation results demonstrate the superiority of the proposed method and unveil an interesting trade-off between EE and SE for two considered scenarios.

In this paper, we propose a light-weight jamming-resistant scheme for the Internet-of-Things (IoT) in 5G networks to ensure high-quality communication in a two-hop cooperative network. In the considered system model, a source communicates with a destination in the presence of an untrusted relay and a powerful multi-antenna adversary jammer. The untrusted relay is an authorized necessary helper who may wiretap the confidential information. Meanwhile, the jammer is an external attacker who tries to damage both the training and transmission phases. Different from traditional frequency hopping spread spectrum (FHSS) techniques that require a pre-determined pattern between communicating nodes, in our scheme, the source and destination enjoy the local observations of the two-hop channels. Then they exploit the measured channel as the source of common randomness to generate shared secret keys. By collecting multiple time slots into a frame, the sequence of channels observed in each frame is utilized to specify the adopted FHSS sequence in the next frame. Based on the derived FHSS sequence from the key generation phase, the source starts to transmit its message supporting by the the destination-assisted cooperative jamming (DACJ) technique which prevents the untrusted relay from discovering the secret message. For the mentioned system model, we present new closed-form expressions for characterizing the achievable secret key rate (SKR) and ergodic secrecy rate (ESR) to highlight the efficiency of our proposed scheme compared to the state-of-the-art. We next determine the optimal power allocation (OPA) between the pilot and data transmission phases that maximizes the ESR performance while escaping from jamming attack. Finally, several numerical examples and discussions are presented to gain engineering insights behind the studied communication scenario.

This paper investigates the wireless-powered hierarchical fog-cloud computing networks, where multiple energy-constrained users harvest energy from a hybrid access point (HAP) firstly and then use their harvested energy to offload their computation tasks to fog/cloud servers via the HAP or compute their tasks locally. To pursue multi-user fairness, an optimization problem is formulated to maximize the minimal energy balance among all users by jointly optimizing time assignments, computation central processing unit (CPU) frequencies, and the computing mode selection. Since the problem is mixed-integer combinatorial non-convex, which is intractable, a generalized Benders decomposition (GBD)-based method is proposed, which guarantees the globally optimal solution. To release the high computational complexity of the proposed GBD-based method, a penalized successive convex approximation (P-SCA)-based algorithm is designed as an alternative to obtain a suboptimal solution with low computational complexity. Numerical results show that among different optimizable factors in the system, computing mode selection is the dominant one on affecting the system performance. Moreover, for each user, local computing is a better choice, if it is with relatively poor channel gain and small local computing delay. Otherwise, fog/cloud computing may be a better choice. Additionally, for the users with relatively high channel gains, if their local computing delays are less than those selecting fog computing, cloud computing should be a better choice.

This paper studies physical-layer secure transmissions from a transmitter to a legitimate receiver against an eavesdropper over slow fading channels, taking into account the impact of finite blocklength secrecy coding. A comprehensive analysis and optimization framework is established to investigate secrecy throughput for both single- and multi-antenna transmitter scenarios. Both adaptive and non-adaptive design schemes are devised, in which the secrecy throughput is maximized by exploiting the instantaneous and statistical channel state information of the legitimate receiver, respectively. Specifically, optimal transmission policy, blocklength, and code rates are jointly designed to maximize the secrecy throughput. Additionally, null-space artificial noise is employed to improve the secrecy throughput for the multi-antenna setup with the optimal power allocation derived. Various important insights are developed. In particular, 1) increasing blocklength benefits both reliability and secrecy under the proposed transmission policy; 2) secrecy throughput monotonically increases with blocklength; 3) secrecy throughput initially increases but then decreases as secrecy rate increases, and the optimal secrecy rate maximizing the secrecy throughput should be carefully chosen in order to strike a good balance between rate and decoding correctness. Numerical results are eventually presented to verify theoretical findings.

This paper investigates the optimal design of wireless- powered hierarchical fog-cloud computing networks, where energy-constrained users first harvest energy from a hybrid access point (HAP) and then offload their computation tasks to fog/cloud servers via the HAP or compute the tasks locally by us- ing the harvested energy. An optimization problem is formulated to maximize the minimal energy balance among multiple users by jointly optimizing offloading decisions, communication and computation resource allocations in the system, where computational capacity, processing delay, energy harvesting (EH) and energy consumption constraints are considered. To efficiently solve such a mixed-integer combinatorial non-convex problem, a penalized successive convex approximation (P-SCA)- based algorithm is designed, which is able to converge to a suboptimal solution with the polynomial time computational complexity. Numerical results show that compared to communication and computation resource allocation, the offloading decision is the dominant factor on affecting the system performance. It is also found that local computing is a better choice for users with relatively poor channel gains while fog/cloud computing is a better choice for users with relatively good channel gains. Specifically, cloud computing is preferred if the cloud computational capacity is strong enough and the wired-link data rate is high enough; Otherwise, fog computing is preferred. Besides, more users are served, less max-min energy balance can be obtained.

In order to support massive connections over limited radio spectrum for the cellular Internet-of-Things (IoT) in the fifth-generation (5G) wireless network, we propose a new non-orthogonal beamspace multiple access framework. First, we analyze the performance of the proposed non-orthogonal beamspace multiple access scheme, and derive an upper bound on the weighted sum rate in terms of channel conditions and system parameters. Then, we provide a transmit beam construction algorithm for further improving the overall performance. Finally, extensive simulation results show that substantial performance gain can be obtained by the proposed non-orthogonal beamspace multiple access scheme over the baseline ones.

This work considers the design of online transmission policy in a wireless-powered communication system with a given energy budget. The system design objective is to maximize the long-term throughput of the system exploiting the energy storage capability at the wireless-powered node. We formulate the design problem as a constrained Markov decision process (CMDP) problem and obtain the optimal policy of transmit power and time allocation in each fading block via the Lagrangian approach. To investigate the system performance in different scenarios, numerical simulations are conducted with various system parameters. Our simulation results show that the optimal policy significantly outperforms a myopic policy which only maximizes the throughput in the current fading block. Moreover, the optimal allocation of transmit power and time is shown to be insensitive to the change of modulation and coding schemes, which facilitates its practical implementation.

In this paper, we propose a beamwidth control-based non-orthogonal multiple access (NOMA) scheme for hybrid millimeter wave (mmWave) communication systems. In particular, the proposed scheme allows multiple users in one NOMA group to share the same radio frequency chain and analog beam for superposition transmission. To overcome the physical limit of the narrow analog beam, a beamwidth control approach is proposed to widen the analog beamwidth to facilitate the formation of NOMA groups. Then, we characterize the main lobe power loss associated with the proposed beamwidth control and derive the asymptotically optimal analog beamformer to maximize the system sum-rate in the large number of antennas regime. The system sum-rate gain of the proposed beamwidth control-based NOMA scheme compared to a baseline scheme adopting time division multiple access (TDMA) is analyzed. Simulation results verify the accuracy of our performance analysis and unveil the importance of beamwidth control for practical mmWave NOMA systems.

This paper proposes a distributed hybrid millimeter wave (mmWave) scheme to exploit the structure of a Densely Deployed Distributed (DDD) small-cell-base-stations (SBSs) system for serving multiple users in a geographic area. Both the SBSs and the users are equipped with full access hybrid architectures with multi-antenna arrays and multiple radio frequency chains. Unlike the conventional cellular networks where users receive data streams from their nearest BSs, the users in our proposed scheme simultaneously receive data streams from different SBSs. With appropriate design of analog beamformers, co-channel multi-data-stream interference can be mitigated and the extra spatial degrees of freedom induced by the geographic distributed SBSs are exploited for data multiplexing. Analytical and simulation results show that the proposed scheme can improve the system sum-rate considerably, especially when the number of scattering components in millimeter wave channels is limited.

In this paper, we study the resource allocation and trajectory design for energy-efficient secure unmanned aerial vehicle (UAV) communication systems where a UAV base station serves multiple legitimate ground users in the existence of a potential eavesdropper. We aim to maximize the energy efficiency of the UAV by jointly optimizing its transmit power, user scheduling, trajectory, and velocity. The design is formulated as a non-convex optimization problem taking into account the maximum tolerable signal-to-noise ratio (SNR) leakage, the minimum data rate requirement of each user, and the location uncertainty of the eavesdropper. An iterative algorithm is proposed to obtain an efficient suboptimal solution. Simulation results demonstrate that the proposed algorithm can achieve a significant improvement of the system energy efficiency while satisfying communication security constraint, compared to some simple scheme adopting straight flight trajectory with a constant speed.

In this paper, we consider the design of joint resource blocks (RBs) and power allocation for dual-mode base stations operating over millimeter wave (mmW) band and microwave (μW) band. The resource allocation design aims to minimize the system energy consumption while taking into account the channel state information, maximum delay, load, and different types of user applications (UAs). To facilitate the design, we first propose a group-based algorithm to assign UAs to multiple groups. Within each group, low-power UAs, which often appear in short distance and experience less obstacles, are inclined to be served over mmW band. The allocation problem over mmW band can be solved by a greedy algorithm. Over μW band, we propose an estimation-optimal-descent algorithm. The rate of each UA at all RBs is estimated to initialize the allocation. Then, we keep altering RB's ownership until any altering makes power increases. Simulation results show that our proposed algorithm offers an excellent tradeoff between low energy consumption and fair transmission.

Wireless transmission of information and power has received growing attention in the research community in the past few years. In two consecutive special issues, a total of thirty papers present state-of-the-art results in the broad area of wireless transmission of information and power.

Radio waves carry both energy and information simultaneously. Nevertheless, radio-frequency (RF) transmissions of these quantities have traditionally been treated separately. Currently, the community is experiencing a paradigm shift in wireless network design, namely, unifying wireless transmission of information and power so as to make the best use of the RF spectrum and radiation as well as the network infrastructure for the dual purpose of communicating and energizing. In this paper, we review and discuss recent progress in laying the foundations of the envisioned dual purpose networks by establishing a signal theory and design for wireless information and power transmission (WIPT) and identifying the fundamental tradeoff between conveying information and power wirelessly. We start with an overview of WIPT challenges and technologies, namely, simultaneous WIPT (SWIPT), wirelessly powered communication networks (WPCNs), and wirelessly powered backscatter communication (WPBC). We then characterize energy harvesters and show how WIPT signal and system designs crucially revolve around the underlying energy harvester model. To that end, we highlight three different energy harvester models, namely, one linear model and two nonlinear models, and show how WIPT designs differ for each of them in single-user and multi-user deployments. Topics discussed include rate-energy region characterization, transmitter and receiver architectures, waveform design, modulation, beamforming and input distribution optimizations, resource allocation, and RF spectrum use. We discuss and check the validity of the different energy harvester models and the resulting signal theory and design based on circuit simulations, prototyping, and experimentation. We also point out numerous directions that are promising for future research.

This second of the two issues on wireless transmission of information and power starts with some works on Simultaneous Wireless Information and Power Transfer (SWIPT), then switches to Wirelessly Powered Communication Networks (WPCNs), and finishes with a few works on Wirelessly Powered Backscatter Communication (WPBC).

In this paper, we address a practical but adverse problem that successive interference cancellation (SIC) is imperfect in a massive non-orthogonal multiple access (NOMA) system. The benefits of a multiple-antenna base station are exploited to support massive access through user clustering in the spatial domain and alleviate the impact of imperfect SIC. In particular, transmit beams and powers are jointly optimized to mitigate the intra-cluster and inter-cluster interference, so as to improve the overall performance in the presence of imperfect SIC. Specifically, we design the joint optimization algorithms from the perspectives of maximizing the weighted sum rate and minimizing the total power consumption, respectively. Moreover, in order to reduce the computational complexity, we design the massive NOMA algorithms with zero-forcing beamforming fixedly. The impacts of imperfect SIC on the design of massive NOMA algorithms are revealed, and it is found that the proposed algorithms are still applicable even if SIC is perfect. Finally, simulations results validate the theoretical claims and show that obvious performance gain can be obtained over the baseline algorithms.

This paper considers the uplink of a cloud radio access network (C-RAN) comprised of several multi-antenna remote radio units (RUs) which compress the signals that they receive from multiple mobile users (MUs) and forward them to a CU via wireless fronthaul links. To enable reliable high rate fronthaul links, we employ a hybrid radio frequency (RF)/free space optical (FSO) system for fronthauling. Moreover, to strike a balance between complexity and performance, we consider three different quantization schemes at the RUs, namely per-antenna vector quantization (AVQ), per-RU vector quantization (RVQ), and distributed source coding (DSC), two different RF fronthaul transmission modes, namely orthogonal transmission and non-orthogonal transmission, and two different detectors at the CU, namely the linear minimum mean square error detector and the optimal successive interference cancellation detector. For this network architecture, we investigate the joint optimization of the quantization noise covariance matrices at the RUs and the RF time allocation to the multiple-access and fronthaul links for rate region maximization. To this end, we formulate a unified weighted sum rate maximization problem valid for each possible combination of the considered quantization, RF fronthaul transmission, and detection schemes. To handle the non-convexity of the unified problem, we transform it into a bi-convex problem which facilitates the derivation of an efficient suboptimal solution using alternating convex optimization and golden section search. Moreover, by introducing a backoff parameter to reduce the probability of infeasibility, we generalize the proposed optimization framework to account for imperfect channel estimation. Our simulation results show that for each combination of the considered quantization, RF fronthaul transmission, and detection schemes, C-RAN with hybrid RF/FSO fronthauling can achieve a considerable sum rate gain compared to conventional systems employing pure FSO fronthauling, especially under unfavorable atmospheric conditions. In addition, employing a more sophisticated quantization scheme can significantly improve the system performance under adverse atmospheric conditions. In contrast, in clear weather conditions, when the FSO link capacity is high, the simple AVQ scheme performs close to the optimal DSC scheme. Furthermore, our simulation results suggest that the proposed algorithm can be adapted to the quality of the channel estimates by tuning the backoff parameter.

In this paper, we investigate the resource allocation algorithm design for multicarrier solar-powered unmanned aerial vehicle (UAV) communication systems. In particular, the UAV is powered by the solar energy enabling sustainable communication services to multiple ground users. We study the joint design of the 3D aerial trajectory and the wireless resource allocation for maximization of the system sum throughput over a given time period. As a performance benchmark, we first consider an off-line resource allocation design assuming non-causal knowledge of the channel gains. The algorithm design is formulated as a mixed-integer non-convex optimization problem taking into account the aerodynamic power consumption, solar energy harvesting, a finite energy storage capacity, and the quality-of-service requirements of the users. Despite the non-convexity of the optimization problem, we solve it optimally by applying monotonic optimization to obtain the optimal 3D-trajectory and the optimal power and subcarrier allocation policy. Subsequently, we focus on the online algorithm design that only requires real-time and statistical knowledge of the channel gains. The optimal online resource allocation algorithm is motivated by the off-line scheme and entails a high computational complexity. Hence, we also propose a low-complexity iterative suboptimal online scheme based on the successive convex approximation. Our simulation results reveal that both the proposed online schemes closely approach the performance of the benchmark off-line scheme and substantially outperform two baseline schemes. Furthermore, our results unveil the tradeoff between solar energy harvesting and power-efficient communication. In particular, the solar-powered UAV first climbs up to a high altitude to harvest a sufficient amount of solar energy and then descends again to a lower altitude to reduce the path loss of the communication links to the users it serves.

In this paper, we consider the uplink channel parameter estimation problem in the presence of pilot contamination for massive multiple-input-multiple-output (MIMO) systems. We propose a parallel factor (PARAFAC)-based estimation scheme, which exploits the low-rank property of massive MIMO channels caused by the finite scattering in a physical environment. Specifically, we first parameterize the channel in terms of three parameters, i.e., fading coefficients, directions of arrival (DOAs), and delays; thereby, the channel is characterized via three equivalent PARAFAC models. Then, the proposed PARAFAC-based scheme is developed, which jointly estimates these three channel parameters using an alternating least squares (ALS) algorithm. Therein, we certify the identifiability of the three channel parameters of the PARAFAC models to mitigate the pilot contamination and state the convergence of the ALS algorithm, which guarantees that the three channel parameters can be uniquely determined with the proposed scheme. Moreover, to further reduce the computational complexity, two advanced schemes are proposed by antenna selection and reducing the estimation frequency of DOAs and delays, respectively. Simulation results show that the proposed schemes can achieve both low computational complexities and close to optimal Cramer-Rao Bound performance.

In this paper, we propose a multi-beam non-orthogonal multiple access (NOMA) scheme for hybrid millimeter wave (mmWave) systems and study its resource allocation. A beam splitting technique is designed to generate multiple analog beams to serve multiple NOMA users on each radio frequency chain. In contrast to the recently proposed single-beam mmWave-NOMA scheme which can only serve multiple NOMA users within the same analog beam, the proposed scheme can perform NOMA transmission for the users with an arbitrary angle-of-departure distribution. This provides a higher flexibility for applying NOMA in mmWave communications and thus can efficiently exploit the potential multi-user diversity. Then, we design a suboptimal two-stage resource allocation for maximizing the system sum-rate. In the first stage, assuming that only analog beamforming is available, a user grouping and antenna allocation algorithm is proposed to maximize the conditional system sum-rate based on the coalition formation game theory. In the second stage, with the zero-forcing digital precoder, a suboptimal solution is devised to solve a non-convex power allocation optimization problem for the maximization of the system sum-rate which takes into account the quality of service constraints. Simulation results show that our designed resource allocation can achieve a close-to-optimal performance in each stage. In addition, we demonstrate that the proposed multi-beam mmWave-NOMA scheme offers a substantial spectral efficiency improvement compared to that of the single-beam mmWave-NOMA and the mmWave orthogonal multiple access schemes.

In this paper, we investigate secure transmission in a massive multiple-input multiple-output system adopting low-resolution digital-to-analog converters (DACs). Artificial noise (AN) is deliberately transmitted simultaneously with the confidential signals to degrade the eavesdropper's channel quality. By applying the Bussgang theorem, a DAC quantization model is developed which facilitates the analysis of the asymptotic achievable secrecy rate. Interestingly, for a fixed power allocation factor φ, low-resolution DACs typically result in a secrecy rate loss, but in certain cases, they provide superior performance, e.g., at low signal-to-noise ratio (SNR). Specifically, we derive a closed-form SNR threshold which determines whether low-resolution or high-resolution DACs are preferable for improving the secrecy rate. Furthermore, a closed-form expression for the optimal φ is derived. With AN generated in the null-space of the user channel and the optimal φ, low-resolution DACs inevitably cause secrecy rate loss. On the other hand, for random AN with the optimal φ, the secrecy rate is hardly affected by the DAC resolution because the negative impact of the quantization noise can be compensated by reducing the AN power. All the derived analytical results are verified by numerical simulations.

In this paper, we investigate the issue of low-cost design of massive access for cellular internet of things (IoT) over spatially correlated Rician fading channels. Specifically, by exploiting a low-overhead transmission protocol, a base station (BS) equipped with a large-scale antenna array and low-resolution analog-to-digital converters (ADCs) is deployed to serve a massive number of IoT devices with low-complexity successive interference cancellation (SIC) receivers. We first analyze the impacts of the low-cost design on the system performance and derive closed-form expressions for uplink and downlink spectral efficiencies of the cellular IoT. Then, for alleviating the negative impacts of the low-cost design, we propose an algorithm allocating the time for channel estimation, uplink data transmission, and downlink data transmission in a data frame. Finally, extensive simulation results confirm the effectiveness of the proposed low-cost design for the cellular IoT.

In this paper, we consider the design of online transmission policies in a single-user wireless-powered communication system over an infinite horizon, aiming at maximizing the long-term system throughput for the user equipment (UE) subject to a given energy budget. The problem is formulated as a constrained Markov decision process problem, which is subsequently converted into an equivalent Markov decision process (MDP) problem via the Lagrangian approach. The corresponding optimal resource allocation policy is obtained through jointly solving the corresponding MDP problem and updating the Lagrangian multiplier. To reduce the complexity, a sub-optimal policy named “quasi-best-effort” is proposed, where the transmit power of the UE is structurally designed so that in each block the UE either exhausts its entire battery energy for transmission or suspends its transmission. To validate the effectiveness of our proposed policy, extensive numerical simulations are conducted with various system parameters. The results show that the proposed quasi-best-effort policy requires far less computation time but achieves a similar long-term throughput performance as the optimal policy.

This paper studies multi-antenna-aided covert communications coexisting with randomly located wardens and interferers, considering both centralized and distributed antenna systems (CAS/DAS). The throughput performance of the covert communication is analyzed and optimized under a stochastic geometry framework, where the joint impact of the small-scale channel fading and the large-scale path loss is examined. To be specific, two probabilistic metrics, namely, the covert outage probability and the connectivity probability, are adopted to characterize the covertness and reliability of the transmission, respectively, and analytically tractable expressions for the two metrics are derived. The worst-case covert communication scenario is then investigated, where the wardens invariably can maximize the covert outage probability by adjusting the detection thresholds for their detectors. Afterward, the optimal transmit power and transmission rate are jointly designed to maximize the covert throughput subject to a covertness constraint. Interestingly, it is found that the maximal covert throughput for both the CAS and DAS is invariant to the density of interferers and the interfering power, regardless of the number of transmit antennas. The numerical results demonstrate that the CAS outperforms the DAS in terms of the covert throughput for the random network of interest, and the throughput gap between the two systems increases dramatically when the number of transmit antennas becomes higher.

In this paper, we investigate the performance gain of non-orthogonal multiple access (NOMA) over orthogonal multiple access (OMA) in uplink single-cell systems. In both single-antenna and multi-antenna scenarios, the performance gain of NOMA over OMA in terms of asymptotic ergodic sumrate is analyzed for a sufficiently large number of users. In particular, in single-antenna systems, we identify two types of near-far gains brought by NOMA: 1) the large-scale near-far gain via exploiting the large-scale fading increases with the cell size; 2) the small-scale near-far gain via exploiting the small-scale fading is a constant given by γ = 0.57721 nat/s/Hz in Rayleigh fading channels. Furthermore, we have analyzed that the performance gain achieved by single-antenna NOMA can be amplified via increasing the number of antennas equipped at the base station due to the extra spatial degrees of freedom. The numerical results confirm the accuracy of the derived analyses and unveil the performance gains of NOMA over OMA in different scenarios.scenarios.

In this paper, we study information-theoretic limits for simultaneous wireless information and power transfer (SWIPT) systems employing a practical nonlinear radio frequency (RF) energy harvesting (EH) receiver. In particular, we consider a three-node system with one transmitter that broadcasts a common signal to separated information decoding (ID) and EH receivers. Owing to the nonlinearity of the EH receiver circuit, the efficiency of wireless power transfer depends significantly on the waveform of the transmitted signal. In this paper, we aim to answer the following fundamental question: What is the optimal input distribution of the transmit waveform that maximizes the rate of the ID receiver for a given required harvested power at the EH receiver? In particular, we study the capacity of a SWIPT system impaired by additive white Gaussian noise (AWGN) under average-power (AP) and peak-power (PP) constraints at the transmitter and an EH constraint at the EH receiver. Using Hermite polynomial bases, we prove that the optimal capacity achieving input distribution that maximizes the rate-energy region is unique and discrete with a finite number of mass points. Our numerical results show that the rate-energy region is enlarged for a larger PP constraint and that the rate loss of the considered SWIPT system compared to the AWGN channel without EH receiver is reduced by increasing the AP budget.

In this paper, we propose a multi-beam non- orthogonal multiple access (NOMA) framework for hybrid millimeter wave (mmWave) systems. The proposed framework enables the use of a limited number of radio frequency (RF) chains in hybrid mmWave systems to accommodate multiple users with various angles of departures (AODs). A beam splitting technique is introduced to generate multiple analog beams to facilitate NOMA transmission. We analyze the performance of a system when there are sufficient numbers of antennas driven by a single RF chain at each transceiver. Furthermore, we derive the sufficient and necessary conditions of antenna allocation, which guarantees that the proposed multi-beam NOMA scheme outperforms the conventional time division multiple access (TDMA) scheme in terms of system sum-rate. The numerical results confirm the accuracy of the developed analysis and unveil the performance gain achieved by the proposed multi- beam NOMA scheme over the single-beam NOMA scheme.

In this paper, we propose a novel joint caching and non-orthogonal multiple access (NOMA) scheme to facilitate advanced downlink transmission for next generation cellular networks. In addition to reaping the conventional advantages of caching and NOMA transmission, the proposed cache-aided NOMA scheme also exploits cached data for interference cancellation which is not possible with separate caching and NOMA transmission designs. Furthermore, as caching can help to reduce the residual interference power, several decoding orders are feasible at the receivers, and these decoding orders can be flexibly selected for performance optimization. We characterize the achievable rate region of cache-aided NOMA and investigate its benefits for minimizing the time required to complete video file delivery. Our simulation results reveal that, compared to several baseline schemes, the proposed cache-aided NOMA scheme significantly expands the achievable rate region for downlink transmission, which translates into substantially reduced file delivery times.

In this paper, we investigate the system performance of a multi-cell multi-user (MU) hybrid millimeter wave (mmWave) multiple-input multiple- output (MIMO) network adopting the channel estimation algorithm proposed in [1] for channel estimation. Due to the reuse of orthogonal pilot symbols among different cells, the channel estimation is expected to be affected by pilot contamination, which is considered as a fundamental performance bottleneck of conventional multicell MU massive MIMO networks. To analyze the impact of pilot contamination on the system performance, we derive the closed-form approximation expression of the normalized mean squared error (MSE) of the channel estimation performance. Our analytical and simulation results show that the channel estimation error incurred by the impact of pilot contamination and noise vanishes asymptotically with an increasing number of antennas equipped at each radio frequency (RF) chain deployed at the desired BS. Thus, pilot contamination is no longer the fundamental problem for multi-cell hybrid mmWave systems.

This paper considers the security issue of the fifth-generation wireless networks with massive connections, where multiple eavesdroppers aim to intercept the confidential messages through active eavesdropping. To realize secure massive access, non-orthogonal channel estimation and non-orthogonal multiple access techniques are combined to enhance the signal quality at legitimate users, while the inter-user interference is harnessed to deliberately confuse the eavesdroppers even without exploiting artificial noise. We first analyze the secrecy performance of the considered secure massive access system and derive a closed-form expression for the ergodic secrecy rate. In particular, we reveal the impact of some key system parameters on the ergodic secrecy rate via asymptotic analysis with respect to a large number of antennas and a high transmit power at the base station. Then, to fully exploit the inter-user interference for security enhancement, we propose to optimize the transmit powers in the stages of channel estimation and multiple access. Finally, extensive simulation results validate the effectiveness of the proposed secure massive access scheme.

This letter considers the minimization of the offloading delay for nonorthogonal multiple access assisted mobile edge computing (NOMA-MEC). By transforming the delay minimization problem into a form of fractional programming, two iterative algorithms based on, respectively, Dinkelbach's method and Newton's method are proposed. The optimality of both methods is proved and their convergence is compared. Furthermore, criteria for choosing between three possible modes, namely orthogonal multiple access, pure NOMA, and hybrid NOMA, for MEC offloading are established.

In this paper, we study the resource allocation algorithm design for multiple-input single-output (MISO) multicarrier non-orthogonal multiple access (MC-NOMA) systems, in which a full-duplex base station serves multiple half-duplex uplink and downlink users on the same subcarrier simultaneously. The resource allocation is optimized for maximization of the weighted system throughput while the information leakage is constrained and artificial noise is injected to guarantee secure communication in the presence of multiple potential eavesdroppers. To this end, we formulate a robust non-convex optimization problem taking into account the imperfect channel state information of the eavesdropping channels and the quality-of-service requirements of the legitimate users. Despite the non-convexity of the optimization problem, we solve it optimally by applying monotonic optimization which yields the optimal beamforming, artificial noise design, subcarrier allocation, and power allocation policy. The optimal resource allocation policy serves as a performance benchmark since the corresponding monotonic optimization-based algorithm entails a high computational complexity. Hence, we also develop a low-complexity suboptimal resource allocation algorithm which converges to a locally optimal solution. Our simulation results reveal that the performance of the suboptimal algorithm closely approaches that of the optimal algorithm. Besides, the proposed optimal MISO NOMA system can not only ensure downlink and uplink communication security simultaneously but also provides a significant system secrecy rate improvement compared with the traditional MISO orthogonal multiple access systems and two other baseline schemes.

In this paper, we design a robust resource allocation algorithm for a wireless-powered communication network (WPCN) taking into account residual hardware impairments (HWIs) at the transceivers, the imperfectness of the channel state information, and the non-linearity of practical radio frequency energy harvesting circuits. In order to ensure power-efficient secure communication, physical layer security techniques are exploited to deliberately degrade the channel quality of a multiple-antenna eavesdropper. The resource allocation algorithm design is formulated as a non-convex optimization problem for minimization of the total power consumption in the network, while guaranteeing the quality of service of the information receivers in terms of secrecy rate. The globally optimal solution of the optimization problem is obtained via a 2-D search and semidefinite programming relaxation. To strike a balance between computational complexity and system performance, a low-complexity iterative suboptimal resource allocation algorithm is also proposed. Numerical results demonstrate that both the proposed optimal and suboptimal schemes can significantly reduce the total system power consumption required for guaranteeing secure communication, and unveil the impact of HWIs on the system performance: 1) residual HWIs create a system performance bottleneck in WPCN in the high transmit/receive power regimes; 2) increasing the number of transmit antennas can effectively reduce the power consumption of wireless power transfer and alleviate the performance degradation due to residual HWIs; and 3) imperfect CSI exacerbates the impact of residual HWIs, which increases the power consumption of both wireless power and wireless information transfer.

This paper considers the application of relay to enhance the performance of massive non-orthogonal multiple access (NOMA) systems and solve the challenge of channel state information acquisition in the case of a massive number of users. First, we design a general framework for a multiple-relay-aided massive NOMA system. Then, we analyze the performance of the multiple-relay-aided massive NOMA system, and derive a closed-form expression for a lower bound on the spectral efficiency. In particular, we reveal the impact of system parameters on the spectral efficiency via asymptotic analysis in three important scenarios, e.g., a large number of antennas at the base station (BS), a high transmit power at the BS or the relays, and a large number of relays. To further improve the spectral efficiency in the context of massive access, we propose two effective schemes to optimize the transmit power at the BS and relays, respectively. Finally, extensive simulation results validate the effectiveness of the proposed multiple-relay-aided massive NOMA scheme.

To achieve spectral-efficient massive access in future wireless networks, this paper proposes a comprehensive fully non-orthogonal communication framework. First, we design a fully non-orthogonal communication scheme which consists of non-orthogonal channel estimation and non-orthogonal multiple access. Then, we analyze the performance of the proposed fully non-orthogonal communication, and derive a tight lower bound on the spectral efficiency in terms of key system parameters and channel conditions. Meanwhile, several novel insights are provided on spectral efficiency via asymptotic analysis in three important cases, i.e., a large number of base station (BS) antennas, a high BS transmit power, and perfect channel state information (CSI) at the BS. Finally, we optimize the performance of the proposed fully non-orthogonal communication and present two simple but efficient optimization algorithms for maximizing the weighted sum of spectral efficiency. Extensive simulation results validate the effectiveness of the proposed schemes.

In this paper, we consider multi-quality multicast beamforming of a video stream from a multi-antenna base station to multiple single-antenna users receiving different qualities of the same video stream, via scalable video coding (SVC). Leveraging the layered structure of SVC and exploiting superposition coding as well as successive interference cancellation, we propose a layer-based multi-quality multicast beamforming scheme. To reduce the computational complexity, we also propose a quality-based multi-quality multicast beamforming scheme, which further utilizes the layered structure of SVC and quality information of all users. Under each scheme, for given quality requirements of all users, we formulate the corresponding optimal beamforming design as a non-convex power minimization problem, and obtain a globally optimal solution for a class of special cases as well as a locally optimal solution for the general case. Then, we show that the minimum total transmission power of the layer-based power minimization problem is the same as that of the quality-based power minimization problem, although the latter incurs a lower computational complexity. Next, we consider the optimal joint layer selection and quality-based multi-quality multicast beamforming design to maximize the total utility representing the satisfaction with the received video quality for all users under a given maximum transmission power budget, which is NP-hard in general. Based on the optimal solution of the quality-based power minimization problem, we develop a greedy algorithm to obtain a near optimal solution. Finally, numerical results show that the proposed solutions achieve better performance than existing solutions.

In this paper, we consider the problem of joint secure routing and transmit power optimization for a multi-hop ad-hoc network under the existence of randomly distributed eavesdroppers following a Poisson point process. Secrecy messages are delivered from a source to a destination through a multi-hop route connected by multiple legitimate relays in the network. Our goal is to minimize the end-to-end connection outage probability under the constraint of a secrecy outage probability threshold, by optimizing the routing path and the transmit power of each hop jointly. We show that the globally optimal solution could be obtained by a two-step procedure where the optimal transmit power has a closed-form and the optimal routing path can be found by Dijkstra's algorithm. Then a friendly jammer with multiple antennas is applied to enhance the secrecy performance further, and the optimal transmit power of the jammer and each hop of the selected route is investigated. This problem can be solved optimally via an iterative outer polyblock approximation with 1-D search algorithm. Furthermore, suboptimal transmit powers can be derived using the successive convex approximation method with a lower complexity. Simulation results show the performance improvement of the proposed algorithms for both non-jamming and jamming scenarios, and also reveal a non-trivial tradeoff between the numbers of hops and the transmit power of each hop for secure routing.

In this paper, we investigate the system performance of a multi-cell multi-user (MU) hybrid millimeter wave communications in a multiple-input multiple-output (MIMO) network. Due to the reuse of pilot symbols among different cells, the performance of channel estimation is expected to be degraded by pilot contamination, which is considered as a fundamental performance bottleneck of conventional multi-cell MU massive MIMO networks. To analyze the impact of pilot contamination to the system performance, we first derive the closed-form approximation of the normalized mean-squared error of the channel estimation algorithm proposed by Zhao et al. over Rician fading channels. Our analytical and simulation results show that the channel estimation error incurred by the impact of pilot contamination and noise vanishes asymptotically with an increasing number of antennas equipped at each radio frequency chain at the desired BS. Furthermore, by adopting zero-forcing precoding in each cell for downlink transmission, we derive a tight closed-form approximation of the average achievable rate per user. Our results unveil that the intra-cell interference and inter-cell interference caused by pilot contamination over Rician fading channels can be mitigated effectively by simply increasing the number of antennas equipped at the desired BS.

In this paper, a novel joint design of beamforming and power allocation is proposed for a multi-cell multiuser multiple-input multiple-output non-orthogonal multiple access network. In this network, base stations adopt coordinated multipoint for downlink transmission. We study a new scenario where the users are divided into two groups according to their quality-of-service requirements, rather than their channel qualities as investigated in the literature. Our proposed joint design aims to maximize the sum rate of the users in one group with the best effort while guaranteeing the minimum required target rates of the users in the other group. The joint design is formulated as a non-convex NP-hard problem. To make the problem tractable, a series of transformations is adopted to simplify the design problem. Then, an iterative suboptimal resource allocation algorithm based on successive convex approximation is proposed. In each iteration, a rank-constrained optimization problem is solved optimally via semidefinite program relaxation. Numerical results reveal that the proposed scheme offers significant sum-rate gains compared to the existing schemes and converges fast to a suboptimal solution.

In this paper, we propose a novel wireless caching scheme to enhance the physical layer security of video streaming in cellular networks with limited backhaul capacity. By proactively sharing video data across a subset of base stations (BSs) through both caching and backhaul loading, secure cooperative joint transmission of several BSs can be dynamically enabled in accordance with the cache status, the channel conditions, and the backhaul capacity. Assuming imperfect channel state information (CSI) at the transmitters, we formulate a two-stage non-convex mixed-integer robust optimization problem for minimizing the total transmit power while providing the quality of service and guaranteeing communication secrecy during video delivery, where the caching and the cooperative transmission policy are optimized in an offline video caching stage and an online video delivery stage, respectively. Although the formulated optimization problem turns out to be NP-hard, low-complexity polynomial-time algorithms, whose solutions are globally optimal under certain conditions, are proposed for cache training and video delivery control. Caching is shown to be beneficial as it reduces the data sharing overhead imposed on the capacity-constrained backhaul links, introduces additional secure degrees of freedom, and enables a power-efficient communication system design. Simulation results confirm that the proposed caching scheme achieves simultaneously a low secrecy outage probability and a high power efficiency. Furthermore, due to the proposed robust optimization, the performance loss caused by imperfect CSI knowledge can be significantly reduced when the cache capacity becomes large.

This paper studies secure video streaming in cache-enabled small cell networks, where some of the cache-enabled small cell base stations (BSs) helping in video delivery are untrusted. Unfavorably, caching improves the eavesdropping capability of these untrusted helpers as they may intercept both the cached and the delivered video files. To address this issue, we propose joint caching and scalable video coding of video files to enable secure cooperative multiple-input multiple-output transmission and, at the same time, exploit the cache memory of both the trusted and untrusted BSs for improving the system performance. Considering imperfect channel state information at the transmitters, we formulate a two-timescale non-convex mixed-integer robust optimization problem to minimize the total transmit power required for guaranteeing the quality of service and secrecy during video streaming. We develop an iterative algorithm based on a modified generalized Benders decomposition to solve the problem optimally, where the caching and the cooperative transmission policies are determined via offline (long-timescale) and online (short-timescale) optimization, respectively. Furthermore, inspired by the optimal algorithm, a low-complexity suboptimal algorithm based on a greedy heuristic is proposed. Simulation results show that the proposed schemes achieve significant gains in power efficiency and secrecy performance compared to several baseline schemes.

In this paper, we design a resource allocation algorithm for a wireless-powered communication network (WPCN) taking into account residual hardware impairments (HWIs) at the transceivers and the non-linearity of radio frequency (RF) energy harvesting (EH) circuits. In order to ensure communication secrecy, physical layer (PHY) security techniques are exploited to deliberately degrade the channel quality of a multiple-antenna eavesdropper. The resource allocation algorithm design is formulated as a non-convex optimization problem for the minimization of the total consumed power in the network, while guaranteeing the quality of service (QoS) of the information receivers (IRs). The globally optimal solution of the optimization problem is obtained via a one-dimensional search and semidefinite programming (SDP) relaxation. Numerical results demonstrate that the proposed scheme can significantly reduce the power consumption of the system compared to a baseline scheme, which assumes ideal hardware.

In this paper, we consider multi-quality multicast of a video stream from a multi-antenna base station (BS) to multiple single-antenna users requiring the video at different quality levels, using scalable video coding (SVC). Leveraging the layered structure of SVC and exploiting superposition coding (SC) as well as successive interference cancelation (SIC), we propose a power-efficient layer-based multi- quality multicast beamforming scheme. To reduce the computational complexity, we also propose a power- efficient quality-based multi-quality multicast beamforming scheme, which further utilizes the layered structure of SVC and quality requirements of all users. Under each scheme, for given quality requirements of all users, we formulate the corresponding beamforming design as a non-convex power minimization problem, and obtain a globally optimal solution for a class of special cases as well as a locally optimal solution for the general case. Then, we show that the minimum total transmission power of the quality-based optimization problem is the same as that of the layer-based optimization problem, although the former requires a lower computational complexity. Finally, numerical results show that the proposed solutions achieve better performance than existing solutions.

This paper considers the uplink of a cloud radio access network (C-RAN) comprised of several multi-antenna remote radio units (RUs) which send the data that they received from multiple mobile users (MUs) to a central unit (CU) via a wireless fronthaul link. One of the fundamental challenges in implementing C-RAN is the huge data rate required for fronthauling. To address this issue, we employ hybrid radio frequency (RF)/free space optical (FSO) systems for the fronthaul links as they benefit from both the large data rates of FSO links and the reliability of RF links. To efficiently exploit the fronthaul capacity, the RUs employ vector quantization to jointly compress the signals received at their antennas. Moreover, due to the limited available RF spectrum, we assume that the RF multiple-access and fronthaul links employ the same RF resources. Thereby, we propose an adaptive protocol which allocates transmission time to the RF multiple-access and fronthaul links in a time division duplex (TDD) manner and optimizes the quantization noise covariance matrix at each RU such that the sum rate is maximized. Our simulation results reveal that a considerable gain in terms of sum rate can be achieved by the proposed protocol in comparison with benchmark schemes from the literature, especially when the FSO links experience unfavorable atmospheric conditions.

In this paper, we propose device-to-device (D2D) overlaying communications with spectrum-power trading where D2D users (DUs) consume transmit power to relay the data of cell-edge cellular users (CUs) for uplink transmission in exchange for bandwidth from CUs for D2D communications. The proposed spectrum-power trading aims at exploiting individual disparities from both the spectrum and the power perspectives. Our goal is to maximize the weighted sum EE (WSEE) of DUs via a joint D2D relay selection, bandwidth allocation, and power allocation while guaranteeing the quality of service of each CU. We show that for a given D2D relay selection, the objective function of the WSEE maximization problem in a fractional form can be transformed into a subtractive-form that is more tractable based on the fractional programming theory. To perform D2D relay selection, we first reveal an important property, which connects the WSEE with both the system-centric EE and the fairness- centric EE. Based on this insight, the D2D relay selection problem is cast into a minimum weighted bipartite matching problem that can be solved efficiently with optimality. Simulation results demonstrate the effectiveness of the proposed scheme and algorithm.

This paper studies secure video streaming in cache-enabled small cell networks, where some of the cache-enabled small cell base stations (BSs) helping in video delivery are untrusted. Unfavorably, caching improves the eavesdropping capability of these untrusted helpers as they may intercept both the cached and the delivered video files. To address this issue, we propose joint caching and scalable video coding (SVC) of video files to enable secure cooperative multiple-input multiple-output (MIMO) transmission and exploit the cache memory of all BSs for improving system performance. The caching and delivery design is formulated as a non-convex mixed-integer optimization problem to minimize the total BS transmit power required for secure video streaming. We develop an algorithm based on the modified generalized Benders decomposition (GBD) to solve the problem optimally. Inspired by the optimal algorithm, a low-complexity suboptimal algorithm is also proposed. Simulation results show that the proposed schemes achieve significant gains in power efficiency and secrecy performance compared to three baseline schemes.

In this paper, we investigate resource allocation for multicarrier communication systems employing a full-duplex base station for serving multiple half-duplex downlink and uplink users simultaneously. We study the joint power and subcarrier allocation design for the maximization of the weighted sum throughput of the system. The algorithm design is formulated as a mixed combinatorial non-convex optimization problem and obtaining the globally optimal solution may require prohibitively high computational complexity. Therefore, a low computational complexity suboptimal iterative algorithm exploiting successive convex approximation is proposed to obtain a locally optimal solution. Simulation results confirm that the proposed suboptimal algorithm obtains a substantial improvement in system throughput compared to various existing baseline schemes.

In this paper, we investigate optimal resource allocation for multicarrier (MC) multiple-input single-output non-orthogonal multiple access (MISO-NOMA) downlink systems. The resource allocation design for the maximization of the weighted system throughput is formulated as a non-convex optimization problem taking into account the quality-of-service requirements of the downlink receivers. We employ monotonic optimization to solve the formulated problem and to obtain the optimal joint precoding and subcarrier allocation policy. The optimal resource allocation policy serves as a performance benchmark due to its high computational complexity. Furthermore, a low-complexity suboptimal resource allocation algorithm is developed and shown to find a locally optimal solution. Our simulation results reveal that the suboptimal algorithm closely approaches the optimal performance. Besides, our results show that MC MISO-NOMA significantly improves the system throughput compared to conventional MC MISO orthogonal multiple access.

This paper studies resource allocation for energy-efficient device-to-device (D2D) communications in an overlay wireless cellular networks, where a D2D transmitter first harvests energy from a base station (BS) and then communicates with a D2D receiver. The resource allocation algorithm design is formulated as a non-convex optimization problem for the maximization of the energy efficiency (bits/Joule) of the D2D system. The proposed problem formulation takes into account the minimum required harvested energy, maximum duration of signal transmission, and minimum required system throughput. Exploiting fractional programming theory, we transform the non-convex problem into a standard convex optimization problem. This allows us to characterize the optimal solution structure of joint time, frequency, and power allocation and to derive an efficient iterative algorithm for obtaining the optimal solution. We reveal that the optimal energy-efficient D2D communication in this framework should occur if the D2D transmitter consumes all the harvested energy from the BS. Simulation results demonstrate the energy efficiency improvement brought by the proposed optimal resource allocation compared to two baseline schemes.

In this paper, we develop a low-complexity channel estimation for hybrid millimeter wave (mmWave) systems, where the number of radio frequency (RF) chains is much less than the number of antennas equipped at each transceiver. The proposed channel estimation algorithm aims to estimate the strongest angle-of-arrivals (AoAs) at both the base station (BS) and the users. Then all the users transmit orthogonal pilot symbols to the BS via these estimated strongest AoAs to facilitate the channel estimation. The algorithm does not require any explicit channel state information (CSI) feedback from the users and the associated signalling overhead of the algorithm is only proportional to the number of users, which is significantly less compared to various existing schemes. Besides, the proposed algorithm is applicable to both non-sparse and sparse mmWave channel environments. Based on the estimated CSI, zero-forcing (ZF) precoding is adopted for multiuser downlink transmission. In addition, we derive a tight achievable rate upper bound of the system. Our analytical and simulation results show that the proposed scheme offer a considerable achievable rate gain compared to fully digital systems, where the number of RF chains equipped at each transceiver is equal to the number of antennas. Furthermore, the achievable rate performance gap between the considered hybrid mmWave systems and the fully digital system is characterized, which provides useful system design insights.

We study the beamforming design for multiuser systems with simultaneous wireless information and power transfer (SWIPT). Employing a practical non-linear energy harvesting (EH) model, the design is formulated as a non-convex optimization problem for the maximization of the minimum harvested power across several energy harvesting receivers. The proposed problem formulation takes into account imperfect channel state information (CSI) and a minimum required signal-to-interference-plus-noise ratio (SINR). The globally optimal solution of the design problem is obtained via the semidefinite programming (SDP) relaxation approach. Interestingly, we can show that at most one dedicated energy beam is needed to achieve optimality. Numerical results demonstrate that with the proposed design a significant performance gain and improved fairness can be provided to the users compared to two baseline schemes.

This paper proposes a novel hybrid downlinkuplink cooperative NOMA (HDU-CNOMA) scheme to achieve a better tradeoff between spectral efficiency and signal reception reliability than the conventional cooperative NOMA schemes. In particular, the proposed scheme enables the strong user to perform a cooperative transmission and an interference-free uplink transmission simultaneously during the cooperative phase, at the expense of a slightly decrease in signal reception reliability at the weak user. We analyze the outage probability, diversity order, and outage throughput of the proposed scheme. Simulation results not only confirm the accuracy of the developed analytical results, but also unveil the spectral efficiency gains achieved by the proposed scheme over a baseline cooperative NOMA scheme and a non-cooperative NOMA scheme.

In this paper, we compare the resource allocation fairness of uplink communications between non-orthogonal multiple access (NOMA) schemes and orthogonal multiple access (OMA) schemes. Through characterizing the contribution of the individual user data rate to the system sum rate, we analyze the fundamental reasons that NOMA offers a more fair resource allocation than that of OMA in asymmetric channels. Furthermore, a fairness indicator metric based on Jain's index is proposed to measure the asymmetry of multiuser channels. More importantly, the proposed metric provides a selection criterion for choosing between NOMA and OMA for fair resource allocation. Based on this discussion, we propose a hybrid NOMA-OMA scheme to further enhance the users fairness. Simulation results confirm the accuracy of the proposed metric and demonstrate the fairness enhancement of the proposed hybrid NOMA-OMA scheme compared to the conventional OMA and NOMA schemes.

This paper aims to provide a comprehensive solution for the design, analysis, and optimization of a multiple-antenna non-orthogonal multiple access (NOMA) system for multiuser downlink communication with both time duplex division and frequency duplex division modes. First, we design a new framework for multiple-antenna NOMA, including user clustering, channel state information (CSI) acquisition, superposition coding, transmit beamforming, and successive interference cancellation. Then, we analyze the performance of the considered system, and derive exact closed-form expressions for average transmission rates in terms of transmit power, CSI accuracy, transmission mode, and channel conditions. For further enhancing the system performance, we optimize three key parameters, i.e., transmit power, feedback bits, and transmission mode. Especially, we propose a low-complexity joint optimization scheme, so as to fully exploit the potential of multiple-antenna techniques in NOMA. Moreover, through asymptotic analysis, we reveal the impact of system parameters on average transmission rates, and hence present some guidelines on the design of multiple-antenna NOMA. Finally, simulation results validate our theoretical analysis, and show that a substantial performance gain can be obtained over traditional orthogonal multiple access technology under practical conditions.

In this paper, the performance of non-orthogonal multiple access (NOMA) is investigated and optimized in a downlink space division multiple access network with a multi-antenna base station and randomly deployed users, under a general channel state information (CSI) limited feedback framework. We first propose a dynamic user scheduling and grouping strategy by leveraging limited feedback. Based on that, an analytical framework is proposed to obtain the outage probability of the network in closed form. The diversity order and the impacts of the number of feedback bits on the outage performance of NOMA are analyzed. Furthermore, the net throughput, which captures the network-wide throughput with the uplink feedback cost considered, is maximized by optimizing the number of feedback bits. Numerical results are demonstrated to verify our analytical findings and show that different from the perfect CSI case, there always exists a performance floor of outage probability in the considered network due to limited feedback. Moreover, the optimal number of feedback bits for net throughput maximization increases as the channel coherence time becomes longer.

In this paper, we develop a low-complexity channel estimation for hybrid millimeter wave (mmWave) systems, where the number of radio frequency (RF) chains is much less than the number of antennas equipped at each transceiver. The proposed mmWave channel estimation algorithm first exploits multiple frequency tones to estimate the strongest angle-of-arrivals (AoAs) at both base station (BS) and user sides for the design of analog beamforming matrices. Then, all the users transmit orthogonal pilot symbols to the BS along the directions of the estimated strongest AoAs in order to estimate the channel. The estimated channel will be adopted to design the digital zero-forcing (ZF) precoder at the BS for the multi-user downlink transmission. The proposed channel estimation algorithm is applicable to both the non-sparse and sparse mmWave channel environments. Furthermore, we derive a tight achievable rate upper bound of the digital ZF precoding with the proposed channel estimation algorithm scheme. Our analytical and simulation results show that the proposed scheme obtains a considerable achievable rate of fully digital systems, where the number of RF chains equipped at each transceiver is equal to the number of antennas. Besides, considering the effect of various types of errors, i.e., random phase errors, transceiver analog beamforming errors, and equivalent channel estimation errors, we derive a closed-form approximation for the achievable rate of the considered scheme. We illustrate the robustness of the proposed channel estimation and multi-user downlink precoding scheme against the system imperfection.

In this paper, we consider a multiple-input multiple-output wireless powered communication network, where multiple users harvest energy from a dedicated power station in order to be able to transmit their information signals to an information receiving station. Employing a practical non-linear energy harvesting (EH) model, we propose a joint time allocation and power control scheme, which takes into account the uncertainty regarding the channel state information (CSI) and provides robustness against imperfect CSI knowledge. In particular, we formulate two non-convex optimization problems for different objectives, namely system sum throughput maximization and the maximization of the minimum individual throughput across all wireless powered users. To overcome the non-convexity, we apply several transformations along with a one-dimensional search to obtain an efficient resource allocation algorithm. Numerical results reveal that a significant performance gain can be achieved when the resource allocation is designed based on the adopted non-linear EH model instead of the conventional linear EH model. Besides, unlike a non-robust baseline scheme designed for perfect CSI, the proposed resource allocation schemes are shown to be robust against imperfect CSI knowledge.

In this paper, we investigate resource allocation algorithm design for multicarrier non-orthogonal multiple access (MC-NOMA) systems employing a full-duplex (FD) base station for serving multiple half-duplex (HD) downlink and uplink users simultaneously. The proposed algorithm is obtained from the solution of a non-convex optimization problem for the maximization of the weighted sum system throughput. We apply monotonic optimization to develop an optimal joint power and subcarrier allocation policy. The optimal resource allocation policy serves as a system performance benchmark due to its high computational complexity. Furthermore, a suboptimal iterative scheme based on successive convex approximation is proposed to strike a balance between computational complexity and optimality. Our simulation results reveal that the proposed suboptimal algorithm achieves a close-to-optimal performance. In addition, FD MC-NOMA systems employing the proposed resource allocation algorithms provide a substantial system throughput improvement compared with conventional HD multicarrier orthogonal multiple access (MC-OMA) systems and other baseline schemes. In addition, our results unveil that FD MC-NOMA systems enable a fairer resource allocation compared with traditional HD MC-OMA systems.

In this paper, we study power-efficient resource allocation for multicarrier non-orthogonal multiple access systems. The resource allocation algorithm design is formulated as a non-convex optimization problem which jointly designs the power allocation, rate allocation, user scheduling, and successive interference cancellation (SIC) decoding policy for minimizing the total transmit power. The proposed framework takes into account the imperfection of channel state information at transmitter and quality of service requirements of users. To facilitate the design of optimal SIC decoding policy on each subcarrier, we define a channel-to-noise ratio outage threshold. Subsequently, the considered non-convex optimization problem is recast as a generalized linear multiplicative programming problem, for which a globally optimal solution is obtained via employing the branch-and-bound approach. The optimal resource allocation policy serves as a system performance benchmark due to its high computational complexity. To strike a balance between system performance and computational complexity, we propose a suboptimal iterative resource allocation algorithm based on difference of convex programming. Simulation results demonstrate that the suboptimal scheme achieves a close-to-optimal performance. Also, both proposed schemes provide significant transmit power savings than that of conventional orthogonal multiple access schemes.

In this paper, we investigate channel estimation and multiuser downlink transmission of a time division duplex massive multiple-input multiple-output (MIMO) system in millimeter wave (mmWave) channels. We propose a tone-based linear search algorithm to facilitate the estimation of angle-of-arrivals (AoAs) of the strongest line-of-sight (SLOS) channel component as well as the scattering components of the users at the base station. Based on the estimated AoAs, we reconstruct the SLOS component and scattering components of the users for downlink transmission. We then derive the achievable rates of maximum-ratio transmission (MRT) and zero-forcing (ZF) precoding based on the SLOS component and the SLOS-plus-scattering components (SLPS), respectively. Taking into account the impact of pilot contamination, our analysis and simulation results show that the SLOS-based MRT can achieve higher data rate than that of the traditional pilot-aided-CSI-based (PAC-based) MRT, under the same mean square errors of channel estimation. As for ZF precoding, the achievable rates of the SLPS-based and the PAC-based are identical. Furthermore, we quantify the achievable rate degradation of the SLOS-based MRT precoding caused by phase quantization errors in the large number of antennas regime. We show that the impact of phase quantization errors on the considered systems cannot be mitigated by increasing the number of antennas and therefore the resolutions of radio frequency phase shifters is critical for the design of efficient mmWave massive MIMO systems.

In this paper, we study energy-efficient resource allocation in the downlink of buffer-aided wireless relay networks. We aim at maximizing the system average energy efficiency while maintaining the queue stability at both the base station (BS) and relays. We formulate the resource allocation design as a novel stochastic network optimization problem and based on the well-known Lyapunov drift-plus-penalty policy and the system constraints, we transform it to an instantaneous non-convex optimization problem to be solved in each time slot. We analyze the instantaneous utility function and propose a novel algorithm to find its optimum point. Based on that, we present an effective distributed strategy to get the globally optimal solution for channel and power allocation. Furthermore, we show that the proposed algorithm can be used as a building block for energy-efficient resource allocation in conventional relay networks, where the relays do not have buffering capability, but the BS queues need to be stabilized. Using extensive simulations, we show that the proposed algorithm is able to provide higher energy efficiency compared with the existing algorithms, while keeping the system queues stable.

In this paper, we study the robust beamforming design in cloud radio access networks, where remote radio heads (RRHs) are connected to a cloud server that performs signal processing and resource allocation in a centralized manner. Different from traditional approaches adopting a concave increasing function to model the utility of a user, we model the utility by a sigmoidal function of the signal-to-interference-plus-noise ratio (SINR) to capture the diminishing utility returns for very small and very large SINRs in real-time applications (e.g., video streaming). Our objective is to maximize the aggregate utility of the users while considering the imperfection of channel state information (CSI), limited backhaul capacity, and minimum quality of service requirements. Because of the sigmoidal utility function and some of the constraints, the formulated problem is non-convex. To efficiently solve the problem, we introduce a maximum interference constraint, transform the CSI uncertainty constraints into linear matrix inequalities, employ convex relaxation to handle the backhaul capacity constraints, and exploit the sum-of-ratios form of the objective function. This leads to an efficient resource allocation algorithm, which outperforms several baseline schemes, and closely approaches a performance upper bound for large CSI uncertainty or large number of RRHs.

In this paper, we investigate device-to-device (D2D) overlaying communications with spectrum-power trading, where D2D users (DUs) consume transmit power to relay cell-edge cellular users (CUs) for uplink transmission in exchange for bandwidth from CUs for D2D communications. The proposed spectrum-power trading aims at exploiting individual disparities from both the spectrum and the power perspectives. Recently, energy efficiency (EE) defined by the ratio of the date rate to the power consumption has become increasingly important for devices due to their limited capacity batteries. As such, our goal is to maximize the weighted sum EE (WSEE) of DUs via a joint D2D relay selection, bandwidth allocation, and power allocation while guaranteeing the quality of service of each CU. Specifically, we study WSEE maximization problems for two different cases, i.e., public-interest DUs and self-interest DUs, depending on whether the DUs are willing to share their obtained bandwidth with each other or not. For the case of public-interest DUs, we show that for a given D2D relay selection, the objective function of the WSEE maximization problem in a fractional form can be transformed into a subtractive form that is more tractable based on the fractional programming theory. To perform D2D relay selection, we first reveal a fundamental relationship between the WSEE and two other EE metrics, i.e., system-centric EE and fairness-centric EE, which, to the best of our knowledge, has never been found in the existing works. Based on this insight, the D2D relay selection problem can be cast as a minimum weighted bipartite matching problem. For the case of self-interest DUs, we show that the corresponding problem can also be solved with optimality by the algorithm proposed for the previous case. Simulation results demonstrate the effectiveness of the proposed algorithm.

This paper investigates the design of precoders for single-user multiple-input multiple-output (MIMO) channels, and, in particular, for finite-alphabet signals. Based on an asymptotic expression for the mutual information of channels exhibiting line-of-sight components and rather general antenna correlations, precoding structures that decompose the general channel into a set of parallel subchannel pairs are proposed. Then, a low-complexity iterative algorithm is devised to maximize the sum mutual information of all pairs. The proposed algorithm significantly reduces the computational load of existing approaches with only minimal loss in performance. The complexity savings increase with the number of transmit antennas and with the cardinality of the signal alphabet, making it possible to support values thereof that were unmanageable with existing solutions. Most importantly, the proposed solution does not require instantaneous channel state information (CSI) at the transmitter, but only statistical CSI.

This paper evaluates the energy efficiency of uplink transmission in heterogeneous cellular networks (HetNets), where fractional power control (FPC) is applied at user equipments (TIEs) subject to a maximum transmit power constraint. We first consider an arbitrary deterministic HetNet and characterize the properties of energy efficiency for TIEs in different path loss regimes, or different access regions. By introducing the notion of transfer path loss, we reveal that, for TIE whose path loss is below the transfer path loss, its energy efficiency highly depends on the value of power control coefficient adopted by FPC. In contrast, for TIE with path loss above the transfer path loss, the uplink energy efficiency asymptotically decreases inversely with path loss, independent of the adopted power control coefficient. Based on these properties, we characterize the optimal power control coefficients for maximizing the energy efficiency of FPC in different access regions. Next, we extend the analysis to stochastic HetNets where TIEs and BSs are distributed as independent Poisson point processes, and investigate the distribution of transmit power for uplink TIEs. Moreover, the probability of truncation outage due to constrained maximal transmit power, as well as the average energy efficiency of TIEs are analytically derived as functions of the BS and TIE densities, power control coefficient, and receiver threshold. Simulation results validate the analytical results, show the consistency between deterministic and stochastic analyses, and suggest suitable power control coefficient for achieving energy efficient uplink transmission by FPC in HetNets.

To keep the hardware costs of future communications systems manageable, the use of low-cost hardware components is desirable. This is particularly true for the emerging massive multiple-input multiple-output (MIMO) systems which equip base stations (BSs) with a large number of antenna elements. However, low-cost transceiver designs will further accentuate the hardware impairments, which are present in any practical communication system. In this paper, we investigate the impact of hardware impairments on the secrecy performance of downlink massive MIMO systems in the presence of a passive multiple-antenna eavesdropper. Thereby, for the BS and the legitimate users, the joint effects of multiplicative phase noise, additive distortion noise, and amplified receiver noise are taken into account, whereas the eavesdropper is assumed to employ ideal hardware. We derive a lower bound for the ergodic secrecy rate of a given user when matched filter data precoding and artificial noise (AN) transmission are employed at the BS. Based on the derived analytical expression, we investigate the impact of the various system parameters on the secrecy rate and optimize both the pilot sets used for uplink training and the AN precoding. Our analytical and simulation results reveal that: 1) the additive distortion noise at the BS may be beneficial for the secrecy performance, especially if the power assigned for AN emission is not sufficient; 2) all other hardware impairments have a negative impact on the secrecy performance; 3) despite their susceptibility to pilot interference in the presence of phase noise, so-called spatially orthogonal pilot sequences are preferable unless the phase noise is very strong; and 4) the proposed generalized null-space AN precoding method can efficiently mitigate the negative effects of phase noise.

In this paper, we investigate a communication system comprised of an energy harvesting (EH) source, which harvests radio frequency (RF) energy from an out-of-band full-duplex relay node and exploits this energy to transmit data to a destination node via the relay node. We assume two scenarios for the battery of the EH source. In the first scenario, we assume that the EH source is not equipped with a battery and thereby cannot store energy. As a result, the RF energy harvested during one symbol interval can only be used in the following symbol interval. In the second scenario, we assume that the EH source is equipped with a battery having unlimited storage capacity in which it can store the harvested RF energy. As a result, the RF energy harvested during one symbol interval can be used in any of the following symbol intervals. For both system models, we derive the channel capacity subject to an average power constraint at the relay and an additional energy transmission cost at the EH source. We compare the derived capacities to the achievable rates of several benchmark schemes. Our results show that using the optimal input distributions at both the EH source and the relay is essential for high performance. Moreover, we demonstrate that neglecting the energy transmission cost at the source can result in a severe overestimation of the achievable performance.

In this paper, we propose beamforming schemes to simultaneously transmit data to multiple information receivers (IRs) while transfering power wirelessly to multiple energy-harvesting receivers (ERs). Taking into account the imperfection of the instantaneous channel state information, we introduce a probabilistic-constrained optimization problem to minimize the total transmit power while guaranteeing data transmission reliability, secure data transmission, and power transfer reliability. As the proposed optimization problem is non-convex and has an infinite number of constraints, we propose two robust reformulations of the original problem adopting safe-convex-approximation techniques. The derived robust formulations are in semidefinite programming forms, hence, they can be effectively solved by standard convex optimization packages. Simulation results confirm the superiority of the proposed approaches to a baseline scheme in guaranteeing transmission security.

In this paper, we investigate the resource allocation algorithm design for multicarrier non-orthogonal multiple access (MC-NOMA) systems. The proposed algorithm is obtained from the solution of a non-convex optimization problem for the maximization of the weighted system throughput. We employ monotonic optimization to develop the optimal joint power and subcarrier allocation policy. The optimal resource allocation policy serves as a performance benchmark due to its high complexity. Furthermore, to strike a balance between computational complexity and optimality, a suboptimal scheme with low computational complexity is proposed. Our simulation results reveal that the suboptimal algorithm achieves a close-to-optimal performance and MC-NOMA employing the proposed resource allocation algorithm provides a substantial system throughput improvement compared to conventional multicarrier orthogonal multiple access (MC-OMA).

In this paper, we study the power-efficient resource allocation for multicarrier non-orthogonal multiple access (MC-NOMA) systems. The resource allocation algorithm design is formulated as a non-convex optimization problem which takes into account the statistical channel state information at transmitter and quality of service (QoS) constraints. To strike a balance between system performance and computational complexity, we propose a suboptimal power allocation and user scheduling with low computational complexity to minimize the total power consumption. The proposed design exploits the heterogeneity of QoS requirement to determine the successive interference cancellation decoding order. Simulation results demonstrate that the proposed scheme achieves a close-to-optimal performance and significantly outperforms a conventional orthogonal multiple access (OMA) scheme.

This paper investigates spectrum-power trading between a small cell (SC) and a macro-cell (MC), where the SC consumes power to serve the macro-cell users (MUs) in exchange for some bandwidth from the MC. Our goal is to maximize the system energy efficiency (EE) of the SC while guaranteeing the quality of service (QoS) of each MU as well as small cell users(SUs). Specifically, given the minimum data rate requirement and the bandwidth provided by the MC, the SC jointly optimizes MU selection, bandwidth allocation, and power allocation while guaranteeing its own minimum required system data rate. The problem is challenging due to the binary MU selection variables and the fractional form objective function. We first show that in order to achieve the maximum system EE, the bandwidth of an MU is shared with at most one SU in the SC. Then, for a given MU selection, the optimal bandwidth and power allocations are obtained by exploiting the fractional programming. To perform MU selection, we first introduce the concept of trading EE. Then, we reveal a sufficient and necessary condition for serving an MU without considering the total power constraint and the minimum data rate constraint. Based on this insight, we propose a low computational complexity MU selection algorithm. Simulation results demonstrate the effectiveness of the proposed scheme.

In this paper, we design a resource allocation algorithm for multiuser simultaneous wireless information and power transfer systems for a realistic non-linear energy harvesting (EH) model. In particular, the algorithm design is formulated as a non-convex optimization problem for the maximization of the long-term average total harvested power at EH receivers subject to quality of service requirements for information decoding receivers. To obtain a tractable solution, we transform the corresponding non-convex sum-of-ratios objective function into an equivalent objective function in parametric subtractive form. This leads to a computationally efficient iterative resource allocation algorithm. Numerical results reveal a significant performance gain that can be achieved if the resource allocation algorithm design is based on the non-linear EH model instead of the traditional linear model.

In this paper, we investigate the resource allocation algorithm design for full-duplex simultaneous wireless information and power transfer (FD-SWIPT) systems. The considered system comprises a FD radio base station, multiple single-antenna half-duplex (HD) users, and multiple energy harvesters equipped with multiple antennas. We propose a multi-objective optimization framework to study the trade-off between uplink transmit power minimization, downlink transmit power minimization, and total harvested energy maximization. The considered optimization framework takes into account heterogeneous quality of service requirements for uplink and downlink communication and wireless power transfer. The non-convex multi-objective optimization problem is transformed into an equivalent rank-constrained semidefinite program (SDP) and solved optimally by SDP relaxation under certain general conditions. The solution of the proposed framework results in a set of Pareto optimal resource allocation policies. Numerical results unveil an interesting trade-off between the considered conflicting system design objectives and reveal the improved power efficiency facilitated by FD in SWIPT systems compared to traditional HD systems.

Network slicing enables mobile virtual network operators (MVNOs) to lease network resources from a mobile network operator (MNO). The cloud radio access network (CRAN) architecture reduces the capital and operational expenditures for the MNO and also facilitates MVNOs running virtual machines on the cloud server. In this paper, we propose a beamforming scheme that coordinates multiple remote radio heads (RRHs) in C-RAN to improve the quality of experience (QoE) of users by maximizing their aggregate weighted quality of service (QoS). We model the QoS of each mobile user by a sigmoidal function and formulate the beamforming design as a non-convex optimization problem. By introducing an interference threshold, we first develop an iterative algorithm to determine a suboptimal solution of the original problem. Based on simulation results, we then show that a suitable interference threshold can be obtained in an off-line manner such that the suboptimal solution is a close-to-optimal solution of the original non-convex problem. Simulation results also show that the proposed scheme can significantly improve the aggregate weighted QoS of the mobile users compared to the traditional design where the weighted system sum rate is maximized.