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.

This letter investigates blocked job offloading in a satellite-ground integrated network, which allows each low-earth orbit (LEO) satellite to execute more jobs on-board instead of sending the raw data to ground users. We propose an approximation offloading model to approximate the satellite-ground integrated network and analyze the average execution time, which is defined as when the user receives the raw data or processed result from the LEO satellite. Furthermore, we propose the maximum equivalent arrival rate and blocked job offloading algorithms. Numerical results validate the approximation model’s effectiveness and the proposed algorithms’ performance advantages and indicate a way to select the proper algorithm.

This paper investigates covert communication of multi-antenna relaying system with probabilistic interference, where a multi-antenna Alice transmits its covert message to a single-antenna Bob through a multi-antenna relay. The relay operates under the amplify-and-forward (AF) protocol, and employs antenna selection based on the relay-Bob channel quality. To enhance the covert communication, a multi-antenna jammer probabilistically transmits jamming signals to degrade Willie’s detection performance. For this system, we formulate the covert communication problem as an optimization framework which maximizes the received signal-to-interference-plus-noise ratio (SINR) at Bob while ensuring covertness constraints, by jointly optimizing the beamforming vectors at Alice and the jammer, the jamming probability, and the relay’s amplification factor. To address this non-convex optimization problem, we first derive Willie’s detection error probabilities for both transmission phases. Next, we analytically determine Willie’s optimal detection threshold and minimum detection error probability by examining their monotonicity and extremal properties. Then, we transform the original problem into a tractable convex form based on these analytical results. We further propose an efficient alternating optimization algorithm to iteratively update the system parameters. Simulation results are finally demonstrated to show the effectiveness of the proposed scheme, showing that increasing the jamming power or the number of antennas at the relay significantly improves Bob’s received SINR.

Optimization of user association in a densely deployed cellular network is usually challenging and even more complicated due to the dynamic nature of user mobility and fluctuation in user counts. While deep reinforcement learning (DRL) emerges as a promising solution, its application in practice is hindered by high trial-and-error costs in real world and unsatisfactory physical network performance during training. Also, existing DRL-based user association methods are typically applicable to scenarios with a fixed number of users due to convergence and compatibility challenges. To address these limitations, we introduce a large vision model (LVM)-enhanced digital twin (DT) for wireless networks and propose a parallel DT-driven DRL method for user association and load balancing in networks with dynamic user counts, distribution, and mobility patterns. To construct this LVM-enhanced DT for DRL training, we develop a zero-shot generative user mobility model, named Map2Traj, based on the diffusion model. Map2Traj estimates user trajectory patterns and spatial distributions solely from street maps. DRL models undergo training in the DT environment, avoiding direct interactions with physical networks. To enhance the generalization ability of DRL models for dynamic scenarios, a parallel DT framework is further established to alleviate strong correlation and non-stationarity in single-environment training and improve data efficiency. Numerical results show that the developed LVM-enhanced DT achieves closely comparable training efficacy to the real environment, and the proposed parallel DT framework even outperforms the single real-world environment in DRL training with nearly 20% gain in terms of cell-edge user performance.

Phase noise (PN) significantly degrades the performance of millimeter-wave (mmWave) wireless communication systems. To mitigate this challenge, we propose the amplitude-phase-sphere block modulation (APSBM) scheme, which integrates amplitude-shift keying, phase-shift keying, and sphere modulation. Exploiting the Wiener random walk characteristic of PN, APSBM carries information on the relative amplitude and phase between consecutive symbols, thereby counteracting the impact of common PN. Immunity to residual PN in the scheme is further enhanced through a three-dimensional spherical constellation and modulation configurations. We also propose flexible detection strategies (joint, parallel, and cascaded) at the receiver to accommodate diverse application scenarios, minimizing the influence of PN while maintaining a low computational burden. Simulation and experimental results demonstrate that in the presence of PN, APSBM outperforms quadrature amplitude modulation (QAM), circular QAM, and spiral modulation, achieving a lower peak-to-average power ratio, a reduced bit error rate, and a higher achievable information rate. The proposed modulation and demodulation scheme is particularly effective under high PN conditions, offering a robust solution for PN mitigation in mmWave wireless communication systems.

In OFDM wireless communications, power amplifier nonlinearity generates significant out-of-band (OOB) emissions and in-band distortion, conventionally requiring large power back-offs to address both issues simultaneously. We propose a forward distortion compensation (FDC) scheme using amplitude-phase-frequency block modulation (APFBM) that decouples OOB emission suppression from in-band distortion management, enabling reliable transmission even under severe power amplifier nonlinearity. At the transmitter, in-band distortion is intentionally introduced to facilitate aggressive OOB emission suppression, while APFBM manages bit mapping and imposes a power-sum constraint on information-carrying subcarriers. At the receiver, this power-sum constraint guides the compensation for in-band distortion, ensuring reliable information recovery. This compensation strategy also relaxes digital pre-distortion (DPD) accuracy requirements, permitting a simpler Sigmoid-based static DPD focused solely on OOB suppression and eliminating feedback circuits. The experimental results show a relative gain of 4.4 dB with a power back-off of 5.2 dB compared to nonlinear transmission based on quadrature amplitude modulation with equivalent processing at 4.9 GHz, while a relative gain of 2.6 dB is achieved at 26.5 GHz. This approach achieves superior energy efficiency and spectral efficiency trade-offs, enhancing coverage and performance in wireless communications.

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).

Operating power amplifiers (PAs) at lower input back-off (IBO) levels is an effective way to improve PA efficiency, but often introduces severe nonlinear distortion that degrades transmission performance. Amplitude-phase-time block modulation (APTBM) has recently emerged as an effective solution to this problem. The intrinsic amplitude and phase constraints of each APTBM block can be leveraged to mitigate PA-induced nonlinear distortion via constraint-guided signal reconstruction. However, existing reconstruction methods apply these constraints only heuristically and statistically, limiting the achievable IBO reduction and PA efficiency improvement. This paper addresses this limitation by decomposing the nonlinear distortion into dominant and residual components, and accordingly develops a novel two-stage signal reconstruction algorithm consisting of coarse and fine reconstruction stages. The coarse reconstruction stage eliminates the dominant distortion by jointly exploiting the APTBM block structure and PA nonlinear characteristics. Subsequently, the fine reconstruction stage minimizes the residual distortion by casting it as a nonconvex optimization problem subject to explicit APTBM constraints, for which a closed-form solution is derived. The proposed algorithm is validated through comprehensive numerical simulations and testbed experiments. Results show that, without compromising transmission quality, the proposed algorithm enables an additional IBO reduction of approximately 5 dB in simulations and 2 dB in experiments over baseline methods, yielding relative PA efficiency improvements of 77.8% and 30.9%, respectively.

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.

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.

In autonomous driving scenarios, limited sensing range of individual autonomous vehicles (AVs) and exponential growth of sensing data have drawn increasing attention. This paper focuses on an integrated system combining communication and computation assistance for sensing enhancement, exploring functional fusion and performance optimization of the autonomous driving integrated sensing, communication, and computation (ISCC) system. Specifically, we first establish a cloud-edge-terminal collaborative ISCC system tailored for autonomous driving. For this system, we model sensing, communication, and computation separately, where the sensing model incorporates task-dependent characteristics, specifically considering the sequential execution of detection and tracking as well as the parallel nature of localization. Given that the collaborative computation between AVs and edge nodes aims to maximize system performance, we formulate a mixed integer nonlinear optimization problem. To solve this problem, we design two independent agents for resource and offloading configuration based on deep reinforcement learning. The former can adaptively allocate resources in each time slot without requiring prior knowledge of task arrival times, while the latter employs a partial offloading strategy to leverage the local computing capabilities of AVs, thereby addressing the limitations of existing approaches that rely on fixed resource allocation or neglect local computation. The simulation results show that the average task completion rate of the proposed scheme is significantly improved, the system cost is notably reduced compared with traditional schemes.

In this paper, we focus on beamforming optimization for a multiuser and multi-target integrated sensing and communication (ISAC) system with non-ideal hardware at both the base station (BS) and the users. Specifically, by taking into account the impact of hardware impairments encountered in practice, we jointly optimize the transmit and receive beamforming at the ISAC BS to maximize the minimum radar output signal-to-interference-plus-noise ratio (SINR) for the multi-target sensing, subject to the constraints of multiuser communication requirements and transmit power limit. The formulated joint optimization is nonconovex and challenging to solve. To address this intricate optimization task, we start with a single-target scenario, for which we propose an optimal solution. In particular, we prove in theory that, even in the presence of general additive Gaussian distortions caused by transceiver hardware impairments, a matched filter (MF) radar receiver and a transmit beamforming determined through beampattern gain maximization criterion are optimal, which follow the same strategies as an ideal scenario with perfect hardware. Subsequently, for a general multi-target scenario, we derive a series of closed-form optimal radar receive beamforming. By substituting these solutions, we achieve an equivalent problem reformulation with respect to the transmit beamforming and propose an iterative algorithm to solve it. We also extend the optimization method to cases involving more realistic hardware impairments. Finally, we evaluate the effectiveness of the proposed algorithms and highlight their notable advantages compared to existing approaches through simulation results.

Despite the success of large language models (LLMs) across domains, their potential for efficient channel state information (CSI) compression and feedback in frequency division duplex (FDD) massive multiple-input multiple-output (mMIMO) systems remains largely unexplored yet increasingly important. In this paper, we propose a novel LLM-based framework for CSI feedback to exploit the potential of LLMs. We first reformulate the CSI compression feedback task as a masked token prediction task that aligns more closely with the functionality of LLMs. Subsequently, we design an information-theoretic mask selection strategy based on self-information, identifying and selecting CSI elements with the highest self-information at the user equipment (UE) for feedback. This ensures that masked tokens correspond to elements with lower self-information, while visible tokens correspond to elements with higher self-information, thus maximizing the accuracy of LLM predictions. Finally, the LLM leverages its robust modeling capabilities to reconstruct complete CSI representations through contextual inference. This self-information-driven masking strategy integrates the LLM-based masked token prediction mechanism into a coherent, information-driven framework. Numerical results indicate that the proposed LLM-based CSI feedback framework significantly outperforms traditional small models in CSI reconstruction accuracy, leading to substantial improvements in communication rates in multi-user MIMO scenarios. This approach has the potential to address the limitations of CSI reconstruction accuracy that restrict multi-user communication rates. Moreover, the method deploys a lightweight network at the UE, with additional network complexity overhead only at the base station (BS). Finally, the method demonstrates strong generalization across different compression ratios and exhibits excellent transfer learning capabilities across various channel scenarios. These findings pave the way for integrating LLMs into next-generation wireless communication systems.

Wireless federated learning (FL) facilitates collaborative training of artificial intelligence (AI) models to support ubiquitous intelligent applications at the wireless edge. However, the inherent constraints of limited wireless resources inevitably lead to unreliable communication, which poses a significant challenge to wireless FL. To overcome this challenge, we propose Sign-Prioritized FL (SP-FL), a novel framework that improves wireless FL by prioritizing the transmission of important gradient information through uneven resource allocation. Specifically, recognizing the importance of descent direction in model updating, we transmit gradient signs in individual packets and allow their reuse for gradient descent if the remaining gradient modulus cannot be correctly recovered. To further improve the reliability of transmission of important information, we formulate a hierarchical resource allocation problem based on the importance disparity at both the packet and device levels, optimizing bandwidth allocation across multiple devices and power allocation between sign and modulus packets. To make the problem tractable, the one-step convergence behavior of SP-FL, which characterizes data importance at both levels in an explicit form, is analyzed. We then propose an alternating optimization algorithm to solve this problem using the Newton-Raphson method and successive convex approximation (SCA). Simulation results confirm the superiority of SP-FL, especially in resource-constrained scenarios, demonstrating up to 9.96% higher testing accuracy on the CIFAR-10 dataset compared to existing methods.

Integrated sensing and communication (ISAC) beamforming designs typically rely on the true sensing parameters which can hardly be achieved. In this work, we present a statistically robust ISAC beamforming design to combat the performance degradation caused by the random target and clutter parameter estimation errors. Specifically, our goal is to maximize the radar output signal-to-interference-plus-noise ratio (SINR) averaged over angle and reflection coefficient estimation errors, while fulfilling the communication users’ SINR requirements. To solve this challenging stochastic optimization problem, we develop an efficient majorization-minimization (MM)-based algorithm. In particular, a semi-closed form solution is derived in each iteration for the single-user case. Simulation results show that, under the same communication constraints, our proposed algorithm outperforms the traditional non-robust designs in terms of the detection probability, and even approaches the clairvoyant scheme with known parameters.

We investigate the communication data enhanced direct position determination (DPD) for uplink multi-antenna orthogonal frequency division multiplexing (OFDM) systems. Different from prior related works, both pilot and unknown data signals are employed to estimate the positions of the mobile station (MS) and the virtual points corresponding to non-line-ofsight (NLOS) paths. To solve the novel localization problem, we propose a space alternating generalized expectation maximization (SAGE) based algorithm which regards the unknown data as latent variables. We further analyze the localization CramerRao bound (CRB) that incorporates the impact of communication data. Simulation results and the CRB analysis verify the remarkable localization accuracy improvement achieved by employing data signals. Moreover, the localization performance is also shown to approach the communication data aware CRB under relatively high signal-to-noise ratios (SNRs).

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.

Unreliable communication is a critical bottleneck for the performance of federated learning (FL) in resourceconstrained wireless networks. To address this issue, we propose a priority-aware transmission strategy, where wireless resources are allocated preferentially based on the importance of data. Specifically, recognizing the crucial role of gradient direction in model updating, we transmit the sign and the modulus of local gradients separately, enabling the reuse of sign packets in the event of erroneous modulus transmission. Furthermore, we introduce a hierarchical resource allocation strategy in the proposed framework, prioritizing key gradients via bandwidth allocation across devices and the sign packet via power allocation at each device. Building upon the theoretical one-step convergence analysis, we formulate the resource allocation optimization problem in an explicit form, which facilitates an alternating optimization algorithm respectively applying the Newton method and technique of successive convex approximation (SCA). Numerical results show the superiority of the proposed scheme in both accuracy and convergence rate compared to existing baselines.

In modern wireless networks, user mobility modeling plays a pivotal role in learning-based network optimization, particularly in tasks such as user association and resource allocation. Traditional random mobility models, e.g., random waypoint and Gauss Markov model, often fail to accurately capture the distribution patterns of users within real-world areas. While trace-based mobility models and advanced learning-based trajectory generation methods offer improvements, they are frequently limited by the scarcity of real-world trajectory data in target areas, primarily due to privacy concerns. This paper introduces Map2Traj, a novel zero-shot trajectory generation method that leverages the diffusion model to capture the intrinsic relationship between street maps and user mobility. With solely the street map of an unobserved area, Map2Traj generates synthetic user trajectories that closely resemble the real-world ones in trajectory pattern and spatial distribution. This enables the creation of high-fidelity individual user channel states and an accurate representation of the overall network user distribution, facilitating effective wireless network optimization. Extensive experiments across multiple regions in Xi'an and Chengdu, China demonstrate the effectiveness of our proposed method for zero-shot trajectory generation. A case study applying Map2Traj to user association and load balancing in wireless networks is also presented to validate its efficacy in network optimization.

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.

The bisimulation metric (BSM) is a powerful tool for computing state similarities within a Markov decision process (MDP), revealing that states closer in BSM have more similar optimal value functions. While BSM has been successfully utilized in reinforcement learning (RL) for tasks like state representation learning and policy exploration, its application to multiple-MDP scenarios, such as policy transfer, remains challenging. Prior work has attempted to generalize BSM to pairs of MDPs, but a lack of rigorous analysis of its mathematical properties has limited further theoretical progress. In this work, we formally establish a generalized bisimulation metric (GBSM) between pairs of MDPs, which is rigorously proven with the three fundamental properties: GBSM symmetry, inter-MDP triangle inequality, and the distance bound on identical states. Leveraging these properties, we theoretically analyse policy transfer, state aggregation, and sampling-based estimation in MDPs, obtaining explicit bounds that are strictly tighter than those derived from the standard BSM. Additionally, GBSM provides a closed-form sample complexity for estimation, improving upon existing asymptotic results based on BSM. Numerical results validate our theoretical findings and demonstrate the effectiveness of GBSM in multi-MDP scenarios.

Superimposed pilot (SIP) schemes face significant challenges in effectively superimposing and separating pilot and data signals, especially in multiuser mobility scenarios with rapidly varying channels. To address these challenges, we propose a novel channel-aware learning framework for SIP schemes, termed CaSIP, that jointly optimizes pilot-data power (PDP) allocation and a receiver network for pilot-data interference (PDI) elimination, by leveraging channel path gain information, a form of large-scale channel state information (CSI). The proposed framework identifies user-specific, resource elementwise PDP factors and develops a deep neural network-based SIP receiver comprising explicit channel estimation and data detection components. To properly leverage path gain data, we devise an embedding generator that projects it into embeddings, which are then fused with intermediate feature maps of the channel estimation network. Simulation results demonstrate that CaSIP efficiently outperforms traditional pilot schemes and state-of-the-art SIP schemes in terms of sum throughput and channel estimation accuracy, particularly under high-mobility and low signal-to-noise ratio (SNR) conditions.

We consider the transmit waveform design for a collocated multiple-input multiple-output (MIMO) radar system, where one-bit analog-to-digital converters (ADCs) are deployed to enable a low-lost and power-efficient hardware implementation. Focusing on improving the target parameter estimation performance, we first derive a novel one-bit Cramér-Rao bound (CRB) metric by exploiting the Bussgang-based linear signal model and the worst-case Gaussian assumption. Then, based on the maximum likelihood principle, we develop a practical one-bit parameter estimation method to approach the derived CRB performance. Next, by minimizing the above one-bit CRB objective subject to a total power constraint, we formulate a transmit waveform optimization problem, which is highly nonconvex due to the one-bit quantization. To solve this problem, a majorization-minimization framework integrated with a projected gradient descent (MMPGD) algorithm is carefully designed whose convergence and complexity analysis are also provided. Finally, numerical results substantiate the tightness of the proposed one-bit CRB and the effectiveness of the MMPGD algorithm, where clear performance improvements can be achieved compared to the existing benchmark schemes.

Digital twin (DT)-driven deep reinforcement learning (DRL) has emerged as a promising paradigm for wireless network optimization, offering safe and efficient training environment for policy exploration. However, in theory existing methods can hardly guarantee real-world performance of DTtrained policies before actual deployment. In this paper, we propose the DT bisimulation metric (DT-BSM), a novel metric based on the Wasserstein distance, to quantify the discrepancy between Markov decision processes (MDPs) in both the DT and the corresponding real-world wireless network environment. We prove that for any DT-trained policy, the sub-optimality of its performance (regret) in the real-world deployment is bounded by a weighted sum of the DT-BSM and its sub-optimality within the MDP in the DT, and a modified DT-BSM based on the total variation distance is introduced to avoid the prohibitive calculation complexity of Wasserstein distance for large-scale wireless network scenarios. Numerical experiments validate this first theoretical finding on the provable and calculable performance bounds for DT-driven DRL.

Hybrid precoding technology is an economically efficient solution for modern millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) communications due to its low hardware complexity and high performance. However, the design of hybrid precoding heavily relies on accurate channel state information (CSI), which requires a large number of pilot signals, thereby degrading system performance. Additionally, the computational complexity of hybrid precoding based on optimization or greedy algorithms increases exponentially with the number of antennas and users. This paper proposes a neural network (NN)-based multiuser hybrid precoding (MHP) framework that leverages the spatial consistency between sub-6G and mmWave channels to reduce pilot overhead while enabling efficient hybrid precoding design. The proposed frame-work consists of two key components: HNet and PNet, each designed to achieve specific objectives. First, HNet employs a convolutional NN (CNN)-based U-Net architecture to extract sub-6G CSI and reconstruct high-frequency mmWave CSI. Then, PNet, specifically designed based on the computational process of hybrid precoding, learns to compute the hybrid precoder from the reconstructed CSI. Simulation results demonstrate that the proposed framework effectively performs channel reconstruction and precoding design, achieving near-optimal hybrid precoding performance while reducing pilot overhead by 87.5%.

In this paper, we study the transmit beamformer design to minimize the weighted sum finite blocklength (FBL) decoding error probability for a downlink multiuser multiple-input single-output (MISO) ultra reliable and low-latency communication (URLLC) system. Since the Gaussian Q-function in the design objective does not admit a closed form, the considered problem is challenging to solve. In order to handle the complicated problem, we first obtain its reformulation via a tight Gaussian Q-function approximation, which is then solved via the majorization-minimization (MM) technique tailored for the reformulated problem. Moreover, in order to further reduce the computational complexity, we first obtain the optimal structure of the transmit beamformer for the original problem, based on which we establish a neural network solution which avoids solving convex problems. Furthermore, we also investigate the extension to the statistically robust beamforming design with the imperfect channel state information (CSI). Simulation results verify the clear performance advantages of the proposed solutions over several state-of-the-art schemes as well as widely used beamforming schemes such as regularized zero-forcing (RZF) precoding in terms of the FBL decoding error probability. Moreover, the low-complexity solutions can effectively approach the proposed MM-based algorithms with much reduced computational costs.

Over-the-air computation (AirComp) has emerged as an essential approach for enabling communication-efficient federated learning (FL) over wireless networks. Nonetheless, the inherent analog transmission mechanism in AirComp-based FL (AirFL) intensifies challenges posed by potential Byzantine attacks. In this paper, we propose a novel Byzantine-robust FL paradigm for over-the-air transmissions, referred to as federated learning with secure adaptive clustering (FedSAC). FedSAC aims to protect a portion of the devices from attacks through zero trust architecture (ZTA) based Byzantine identification and adaptive device clustering. By conducting a one-step convergence analysis, we theoretically characterize the convergence behavior with different device clustering mechanisms and uneven aggregation weighting factors for each device. Building upon our analytical results, we formulate a joint optimization problem for the clustering and weighting factors in each communication round. To facilitate the targeted optimization, we propose a dynamic Byzantine identification method using historical reputation based on ZTA. Furthermore, we introduce a sequential clustering method, transforming the joint optimization into a weighting optimization problem without sacrificing the optimality. To optimize the weighting, we capitalize on the penalty convex-concave procedure (P-CCP) to obtain a stationary solution. Numerical results substantiate the superiority of the proposed FedSAC over existing methods in terms of both test accuracy and convergence rate.

Task-oriented integrated sensing, communication, and computation (ISCC) is a key technology for achieving low-latency edge inference and enabling efficient implementation of artificial intelligence (AI) in industrial cyber-physical systems (ICPS). However, the constrained energy supply at edge devices has emerged as a critical bottleneck. In this paper, we propose a novel energy-efficient ISCC framework for AI inference at resource-constrained edge devices, where adjustable split inference, model pruning, and feature quantization are jointly designed to adapt to diverse task requirements. A joint resource allocation design problem for the proposed ISCC framework is formulated to minimize the energy consumption under stringent inference accuracy and latency constraints. To address the challenge of characterizing inference accuracy, we derive an explicit approximation for it by analyzing the impact of sensing, communication, and computation processes on the inference performance. Building upon the analytical results, we propose an iterative algorithm employing alternating optimization to solve the resource allocation problem. In each subproblem, the optimal solutions are available by respectively applying a golden section search method and checking the Karush-Kuhn-Tucker (KKT) conditions, thereby ensuring the convergence to a local optimum of the original problem. Numerical results demonstrate the effectiveness of the proposed ISCC design, showing a significant reduction in energy consumption of up to 40% compared to existing methods, particularly in low-latency scenarios.

A large proportion of the carbon emissions associated with wireless communications stem from electricity consumption during operation. Spectral efficiency (SE) and energy efficiency (EE) are fundamental considerations in wireless communications. However, nonlinear amplification results in a trade-off between these factors. Various techniques have been developed to address this issue, including the frequently-used amplifier linearization. Nonetheless, these approaches have limitations in terms of versatility and complexity, making them impractical for modern broadband multiantenna wireless communications. Here, an amplitude-phase-time block modulation (APTBM) scheme and a corresponding demodulation scheme for resisting amplifier nonlinearity are proposed, establishing a new paradigm for balancing the SE and EE. At the transmitter, the symbol block, consisting of two time-domain consecutive symbols, is used to carry information. Simultaneously, specific amplitude and phase constraints are imposed on the symbols within a block. At the receiver, nonlinearly distorted symbols can be effectively demodulated by utilizing these constraints. Numerical and experimental results show that the proposed APTBM demonstrates excellent nonlinear transmission characteristics compared with conventional offset quadrature amplitude modulation.

Reconfigurable intelligent surface (RIS)-assisted cell-free (CF) multiple-input multiple-output (MIMO) networks can significantly enhance system performance. However, the extensive deployment of RIS elements imposes considerable channel acquisition overhead, with the high density of nodes and antennas in RIS-assisted CF networks amplifying this challenge. To tackle this issue, in this paper, we explore integrating RIS-user equipment (UE) association into downlink RIS-assisted CF transmitter design, which greatly reduces the channel acquisition costs. The key point is that once UEs are associated with specific RISs, there is no need to frequently acquire channels from non-associated RISs. Then, we formulate the problem of joint RIS-UE association and beamforming at APs and RISs to maximize the weighted sum rate (WSR). In particular, we propose a two-stage framework to solve it. In the first stage, we apply a many-to-many matching algorithm to establish the RIS-UE association. In the second stage, we introduce a sequential optimization-based method that decomposes the joint optimization of RIS phase shifts and AP beamforming into two distinct subproblems. To optimize the RIS phase shifts, we employ the majorization-minimization (MM) algorithm to obtain a semi-closed-form solution. For AP beamforming, we develop a joint block diagonalization algorithm, which yields a closed-form solution. Simulation results demonstrate the effectiveness of the proposed algorithm and show that, while RIS-UE association significantly reduces overhead, it incurs a minor performance loss that remains within an acceptable range. Additionally, we investigate the impact of RIS deployment and conclude that RISs exhibit enhanced performance when positioned between APs and UEs.

Integrated sensing and communication (ISAC) enabled by millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) technologies is envisioned to be a promising candidate for future wireless systems. In this paper, we study the mmWave massive MIMO aided ISAC transceiver design with one-bit digital-to-analog converters (DACs) and one-bit analog-to-digital converters (ADCs) for reduced hardware complexity and power consumption. First, we develop a novel one-bit target detector based on the Bussgang decomposition, which fully exploits the spatial correlation at the receiver side for performance enhancement. Then, we derive the detection probability and the false alarm probability of the proposed detector in closed forms, from which we establish an interesting relationship between the detection performance and a new signal-to-quantization-plus-interference-plus-noise ratio (SQINR) metric. Furthermore, we formulate a one-bit ISAC transceiver optimization problem by incorporating both the communication mean-squared error (MSE) and proposed sensing SQINR metrics into the objective function. To address the complicated discrete optimization, we propose an efficient alternating optimization framework embedded with a majorization-minimization (AOMM) algorithm with guaranteed convergence. Finally, extensive simulations are conducted which confirm the validity of our one-bit detection performance analysis and show that the proposed detector outperforms a recent solution relying on the low input signal-to-noise/interference-to-noise ratio (LIS) assumption. Moreover, the proposed ISAC transceiver design can achieve excellent communication and sensing performances with acceptable complexity.

In this paper, we focus on the decoding error probability based beamforming design for a downlink multiantenna multiuser ultra reliable and low-latency communication (URLLC) system. We first optimize the transmit beamformer to minimize the maximum finite blocklength (FBL) decoding error probability under block fading channels, which turns out to be a complicated nonconvex problem with a non-closed-form objective function. A successive convex approximation (SCA)-based algorithm is developed to determine a high-quality solution. Moreover, in order to reduce the computational complexity of the proposed algorithm, we perform the downlink beamforming optimization by solving a simpler virtual uplink problem. The complexity of the resultant solution only scales linearly with the number of transmit antennas. Furthermore, a special case with quasi-static channels is investigated, where the proposed SCA and the uplink-downlink duality based solutions for the block fading channel can be simplified in a non-trivial manner. Simulation results verify the performance superiorities of the proposed algorithms in the context of short packet communication. In particular, the proposed designs outperform conventional regularized zero-forcing (RZF) and max-min signal-to-interference-plus-noise ratio (SINR) beamforming by evident gains in terms of the FBL decoding error probability.

The increasing demands for spectral resources in emerging massive machine-type communication applications necessitate the implementation of massive grant-free transmission in the millimeter-wave (mmWave) band. This paper proposes two efficient receive analog beamforming design algorithms for mmWave massive grant-free transmission under hybrid beamforming architectures, intending to optimize spectral efficiency and access probability, respectively. Specifically, we first express the spectral efficiency of mmWave massive grant-free transmission systems and then derive an analytically tractable approximation using the random matrix theory. Following this, an alternating optimization method is employed to design the receive beamforming matrix efficiently. Additionally, we provide the formulation of access probability for mmWave massive grant-free transmission, whose explicit expression is approximately derived through the Gaussian approximation. Building upon this, we utilize a convex hull relaxation-based optimization method to optimize the beamforming matrix. The effectiveness of our proposed beamforming design algorithms in improving spectral efficiency and access probability is validated through extensive simulation experiments.

The proliferation of diverse wireless services has led to emerging technologies for network slicing. Admission control plays a crucial role in achieving service-oriented goals in sliced communication networks through selective acceptance of service requests. To enhance the performance of admission control in intricate contemporary communication networks, deep reinforcement learning (DRL) has been widely adopted to enhance the effectiveness and flexibility of intelligent admission control. However, due to the simulation-to-reality gap, DRL models trained in a simulation environment can face considerable performance degradation when transferred to the deployment environment. Although online DRL tries to avoid such gaps, its expensive trial-and-error cost poses economic and safety concerns for network operators. We propose a cooperative framework integrating digital twin (DT) and online DRL to address this issue. Specifically, a behavior cloning-based DT is established to parameterize a default admission control policy in real networks, and a DT-accelerated online DRL strategy is then developed for further policy optimization. The DT is constructed as a neural network, featuring a customized output layer to address extensive action spaces in queuing systems. Extensive simulations show that the proposed DRL solution facilitates the stability of the online DRL and accelerates the convergence, yielding a resource utilization improvement of up to 26.39% compared to the state-of-the-art DRL model, while maintaining consistent performance with the online DRL method in terms of long-term revenues. Meanwhile, the proposed solution is versatile and adaptable to various DRL-based network optimization tasks.

Semantic communication is envisioned as a key technology for future wireless networks due to its high communication efficiency. However, research combining semantic communication and advanced multiple access techniques, such as rate splitting multiple access (RSMA), is still lacking. In this paper, the problem of joint communication and computation resource allocation for probabilistic semantic communication (PSCom) with RSMA is investigated. In the considered model, the base station (BS) needs to transmit a large amount of data to multiple users with 1-layer RSMA. Due to limited communication resources, the BS is required to utilize semantic communication techniques to compress the original data. In this paper, we utilize knowledge graphs to represent semantic information and employ probabilistic graphs, which are shared between the BS and users, to further compress the knowledge graphs. The BS can use the probabilistic graph to compress the data to be transmitted, while the user can recover the compressed semantic information using the same shared probabilistic graph. The additional computation power required for semantic information compression inevitably results in a reduction in transmission power due to the limited total power budget. Considering the effect of semantic compression ratio, the semantic rate expression for RSMA is first obtained. Then, based on the obtained rate expression, an optimization problem is formulated with the aim of maximizing the sum of semantic rates of all users under total power, semantic compression ratio, and rate allocation constraints. To tackle this problem, an iterative algorithm is proposed, where the semantic compression ratio subproblem is addressed using a greedy algorithm, and the rate allocation and transmit beamforming design subproblem is solved using a successive convex approximation method. Numerical results validate the effectiveness of the proposed scheme.

Task-oriented semantic communication enhances transmission efficiency by conveying semantic information rather than exact messages. Deep learning (DL)-based semantic communication can effectively cultivate the essential semantic knowledge for semantic extraction, transmission, and interpretation by leveraging massive labeled samples for downstream task training. In this paper, we propose a self-supervised learning-based semantic communication framework (SLSCom) to enhance task inference performance, particularly in scenarios with limited access to labeled samples. Specifically, we develop a task-relevant semantic encoder using unlabeled samples, which can be collected by devices in real-world edge networks. To facilitate task-relevant semantic extraction, we introduce self-supervision for learning contrastive features and formulate the information bottleneck (IB) problem to balance the tradeoff between the informativeness of the extracted features and task inference performance. Given the computational challenges of the IB problem, we devise a practical and effective solution by employing self-supervised classification and reconstruction pretext tasks. We further propose efficient joint training methods to enhance end-to-end inference accuracy over wireless channels, even with few labeled samples. We evaluate the proposed framework on image classification tasks over multipath wireless channels. Extensive simulation results demonstrate that SLSCom significantly outperforms conventional digital coding methods and existing DL-based approaches across varying labeled data set sizes and SNR conditions, even when the unlabeled samples are irrelevant to the downstream tasks.

Federated edge learning (FEEL) enables privacy-preserving model training through periodic communication between edge devices and the server. Unmanned Aerial Vehicle (UAV)-mounted edge devices are particularly advantageous for FEEL due to their flexibility and mobility in efficient data collection. In UAV-assisted FEEL, sensing, computation, and communication are coupled and compete for limited onboard resources, and UAV deployment also affects sensing and communication performance. Therefore, the joint design of UAV deployment and resource allocation is crucial to achieving the optimal training performance. In this paper, we address the problem of joint UAV deployment design and resource allocation for FEEL via a concrete case study of human motion recognition based on wireless sensing. We first analyze the impact of UAV deployment on the sensing quality and identify a threshold value for the sensing elevation angle that guarantees a satisfactory quality of data samples. Due to the non-ideal sensing channels, we consider the probabilistic sensing model, where the successful sensing probability of each UAV is determined by its position. Then, we derive the upper bound of the FEEL training loss as a function of the sensing probability. Theoretical results suggest that the convergence rate can be improved if UAVs have a uniform successful sensing probability. Based on this analysis, we formulate a training time minimization problem by jointly optimizing UAV deployment, integrated sensing, computation, and communication (ISCC) resources under a desirable optimality gap constraint. To solve this challenging mixed-integer non-convex problem, we apply the alternating optimization technique, and propose the bandwidth, batch size, and position optimization (BBPO) scheme to optimize these three decision variables alternately. Simulation results demonstrate that our BBPO scheme outperforms other baseline schemes regarding convergence rate and testing accuracy. The simulation implementation is available at https://github.com/TheaSherlock/ISCC-UAV.

Space-air-ground integrated networks (SAGINs) are emerging as a pivotal element in the evolution of future wireless networks. Despite their potential, the joint design of communication and computation within SAGINs remains a formidable challenge. In this paper, the problem of energy efficiency in SAGIN-enabled probabilistic semantic communication (PSCom) system is investigated. In the considered model, a satellite needs to transmit data to multiple ground terminals (GTs) via an unmanned aerial vehicle (UAV) acting as a relay. During transmission, the satellite and the UAV can use PSCom technique to compress the transmitting data, while the GTs can automatically recover the missing information. The PSCom is underpinned by shared probabilistic graphs that serve as a common knowledge base among the transceivers, allowing for resource-saving communication at the expense of increased computation resource. Through analysis, the computation overhead function in PSCom is a piecewise function with respect to the semantic compression ratio. Therefore, it is important to make a balance between communication and computation to achieve optimal energy efficiency. The joint communication and computation problem is formulated as an optimization problem aiming to minimize the total communication and computation energy consumption of the network under latency, power, computation capacity, bandwidth, semantic compression ratio, and UAV location constraints. To solve this non-convex non-smooth problem, we propose an iterative algorithm where the closed-form solutions for computation capacity allocation and UAV altitude are obtained at each iteration. Numerical results show the effectiveness of the proposed algorithm.

Cost-effective phase noise (PN) compensation is essential for orthogonal frequency division multiplexing (OFDM) systems, especially in millimeter-wave (mmWave) communications. In this paper, we propose an intelligent semi-blind receiver design for PN-affected OFDM systems. Specifically, we first formulate a joint channel estimation, PN compensation, and data detection problem in the time domain, accounting for the constant modulus constraint of PN and discrete constraint of the data. To address the constrained non-convex problem, a low-complexity receiver based on the alternating direction method of multipliers (ADMM) and minorization-maximization (MM) is proposed. Subsequently, by adopting the deep unfolding technique, we present an intelligent receiver to avoid cumbersome parameter selection and also accelerate the algorithm convergence. Simulation results validate the clear performance superiority of our proposed design over existing schemes. Moreover, the complexity per iteration/layer is as low as O(N log2N) with N being the number of subcarriers.

In this paper, the energy efficiency maximization problem in space-air-ground integrated network (SAGIN)-enabled probabilistic semantic communication (PSC) is investigated. In the considered model, a satellite needs to transmit data to multiple ground terminals (GTs) via an unmanned aerial vehicle (UAV) acting as a relay. During transmission, the satellite and the UAV can use PSC technique to compress the transmitted data, while the GTs can automatically recover the original data. In the considered PSC system, shared probability graphs serve as a common knowledge base among the transceivers, allowing for resource-saving communication at the expense of increased computation resource. Therefore, it is important to study the trade-off between communication and computation to achieve optimal energy efficiency. The joint communication and computation problem is formulated as an optimization problem aiming to minimize the total communication and computation energy consumption of the network under latency, semantic compression ratio, and UAV location constraints. To solve this non-convex problem, we propose an alternating algorithm. Numerical results show the effectiveness of the proposed algorithm.

Unmanned Aerial Vehicle (UAV)-mounted edge devices are particularly advantageous for federated edge learning (FEEL) due to their flexibility and mobility in efficient data collection. In UAV-assisted FEEL, sensing, computation, and communication are coupled and compete for limited onboard resources, and UAV deployment also affects sensing and communication performance. Therefore, the joint design of UAV deployment and resource allocation is crucial to achieving the optimal training performance. In this paper, we address the problem of joint UAV deployment design and resource allocation for FEEL via a concrete case study of human motion recognition based on wireless sensing. Due to the nonideal sensing channels, we consider the probabilistic sensing model. Then, we derive the upper bound of the FEEL training loss as a function of the sensing probability. We formulate a training time minimization problem by jointly optimizing UAV deployment, integrated sensing, computation, and communication (ISCC) resources under a desirable optimality gap constraint. To solve this challenging mixed-integer non-convex problem, we propose our algorithm based on the alternating optimization technique. Simulation results demonstrate that our algorithm outperforms other baselines regarding convergence rate and testing accuracy.

This paper investigates the joint design of transmit waveform and receive filter for a collocated multipleinput multiple-output (MIMO) radar, where one-bit digitalto-analog converters (DACs) and one-bit analog-to-digital converters (ADCs) are employed to reduce the hardware cost and power consumption. We first derive a novel signal-toquantization-plus-interference-plus-noise ratio (SQINR) metric via a careful theoretical analysis to accurately characterize the one-bit MIMO radar performance. Then, we formulate a onebit transceiver optimization problem by maximizing the SQINR objective subject to binary DAC constraints. To handle the complicated mixed-integer problem, we propose an efficient penalty-based majorization-minimization (PMM) algorithm with guaranteed convergence. As validated via simulations, the proposed algorithm achieves a noticeable performance gain over the existing benchmark scheme based on a low input signal-to-noise/interference-to-noise ratio (LIS) assumption.

In this paper, we quantitatively compare these two effective communication schemes, i.e., digital and analog ones, for wireless federated learning (FL) over resource-constrained networks, highlighting their essential differences as well as their respective application scenarios. We first examine both digital and analog transmission methods, together with a unified and fair comparison scheme under practical constraints. A universal convergence analysis under various imperfections is established for FL performance evaluation in wireless networks. These analytical results reveal that the fundamental difference between the two paradigms lies in whether communication and computation are jointly designed or not. The digital schemes decouple the communication design from specific FL tasks, making it difficult to support simultaneous uplink transmission of massive devices with limited bandwidth. In contrast, the analog communication allows over-the-air computation (AirComp), thus achieving efficient spectrum utilization. However, computation-oriented analog transmission reduces power efficiency, and its performance is sensitive to computational errors. Finally, numerical simulations are conducted to verify these theoretical observations.

The widespread adoption of Network Address Translation (NAT) technology has led to a significant number of network end nodes being located in private networks behind NAT devices, impeding direct communication between these nodes. To solve this problem, a technique known as "hole punching" has been devised for NAT traversal to facilitate peer-to-peer communication among end nodes located in distinct private networks. However, as the increasing demands for speed and security in networks, TCP-based hole punching schemes gradually show performance drawbacks. Therefore, we present a QUIC-based hole punching scheme for NAT traversal. Through a comparative analysis of the hole punching time between QUIC-based and TCP-based protocols, we find that the QUIC-based scheme effectively reduces the hole punching time, exhibiting a pronounced advantage in weak network environments. Furthermore, in scenarios where the hole punched connection is disrupted due to factors such as network transitions or NAT timeouts, this paper evaluates two schemes for restoring the connection: QUIC connection migration and re-punching. Our results show that QUIC connection migration for connection restoration saves 2 RTTs compared to QUIC re-punching, and 3 RTTs compared to TCP re-punching, effectively reducing the computational resources consumption for re-punching.

In this paper, the problem of joint transmission and computation resource allocation for probabilistic semantic communication (PSC) network with rate splitting multiple access (RSMA) is investigated. In the considered model, the base station (BS) needs to transmit a large amount of data, which is represented by substantial knowledge graphs, to multiple users. Due to limited communication resource, the BS needs to utilize semantic communication techniques to compress the large-sized data. In this paper, the semantic communication is enabled by shared probability graphs between the BS and users. The process of semantic compression requires computation power at the BS, which has an impact on limited power budget. Therefore, it is necessary to balance the power between transmission and computation. Based on the probability graph, the semantic rate related to semantic compression ratio is first theoretically formulated. Then, the problem is formulated as an optimization problem with the aim of maximizing the sum semantic rate of all users under total power, semantic compression ratio, and rate allocation constraints. To tackle this problem, an iterative algorithm is accordingly proposed to obtain a suboptimal solution. Numerical results validate the effectiveness of the proposed scheme.

In this paper, we investigate a multiuser integrated sensing and communication (ISAC) system with hardware im-pairments at both the base station (BS) transceiver and the users. Specifically, by considering the impact of hardware impairments, we optimize the transmit and receive beamforming at the ISAC BS to maximize the radar output signal-to-interference-plus-noise ratio (SINR) for sensing, under both point and extended target scenarios, subject to the constraints of communication requirement and power limitation. For both scenarios, we first find closed-form optimal radar receive beamforming and then obtain equivalent reformulations with respect to the transmit beamforming. Subsequently, for the resulting problems, a globally optimal solution is obtained for the point target scenario and an iterative solution is proposed for the extended target scenario. Finally, the effectiveness of the proposed methods is evaluated via simulation results.

This paper investigates the secure resource allocation for a downlink integrated sensing and communication system with multiple legal users and potential eavesdroppers. In the considered model, the base station (BS) simultaneously transmits sensing and communication signals through beamforming design, where the sensing signals can be viewed as artificial noise to enhance the security of communication signals. To further enhance the security in the semantic layer, the semantic information is extracted from the original information before transmission. The user side can only successfully recover the received information with the help of the knowledge base shared with the BS, which is stored in advance. Our aim is to maximize the sum semantic secrecy rate of all users while maintaining the minimum quality of service for each user and guaranteeing overall sensing performance. To solve this sum semantic secrecy rate maximization problem, an iterative algorithm is proposed using the alternating optimization method. The simulation results demonstrate the superiority of the proposed algorithm in terms of secure semantic communication and reliable detection.

With the global promotion of fifth-generation (5G) communications, researches for sixth generation (6G) communications are being globally launched from various perspectives. Industrial Internet of Things (IIoT), which is the most representative application that reflects the ubiquitous connectivity characteristics of 6G, has attracted much attention. To explore for a feasible perspective in promoting ubiquitous connectivity and improving IIoT abilities, in this article, we seek solutions to incorporate two promising techniques, i.e., nonorthogonal multiple access (NOMA) and statistical signal transmission (SST). To accomplish such a motivation, we conceive the feasible transceiver architecture for the union of NOMA-SST technique, and elaborate workflow and detection procedures for both uplink and downlink modes. A dedicated resilient window strategy is designed thereafter, which largely strengthen weaker NOMA users’ SST performance without depressing stronger users. The proposed technique is finally testified through both simulation experiments and practical applications. Numerical results verify that NOMA-SST technique has satisfactory detection performance in common IIoT environments. It is also manifested that the proposed technique can refine various aspects, including recall rate and sensing accuracy of monitoring services, successful handling rate of emergency situations, etc., for practical IIoT applications. Such advantages are promising for practice.

The advent of Industry 4.0 has positioned the industrial Internet of Things (IIoT) as a cornerstone of future industry. In this article, the problem of spectral-efficient communication and computation resource allocation for distributed reconfigurable intelligent surfaces (RISs) assisted probabilistic semantic communication (PSC) in IIoT is investigated. In the considered model, multiple RISs are deployed to serve multiple users, while PSC adopts compute-then-transmit protocol to reduce the size of the transmission data. To support the high-rate transmission, the semantic compression ratio, transmit power allocation, and distributed RISs deployment must be jointly considered. This joint communication and computation problem is formulated as an optimization problem whose goal is to maximize the sum semantic-aware transmission rate of the system under the total transmit power, phase shift, RIS-user association, and semantic compression ratio constraints. To solve this problem, a many-to-many matching scheme is proposed to solve the RIS-user association subproblem, the semantic compression ratio subproblem is addressed following the greedy policy, while the phase shift of RIS can be optimized using the tensor-based beamforming. Numerical results verify the superiority of the proposed algorithm.

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.

Reconfigurable intelligent surface (RIS) is a promising technique to improve the performance of future wireless communication systems at low energy consumption. To reap the potential benefits of RIS-aided beamforming, it is vital to enhance the accuracy of channel estimation. In this paper, we consider an RIS-aided multiuser system with non-ideal reflecting elements, each of which has a phase-dependent reflecting amplitude, and we aim to minimize the mean-squared error (MSE) of the channel estimation by jointly optimizing the training signals at the user equipments (UEs) and the reflection pattern at the RIS. As examples the least squares (LS) and linear minimum MSE (LMMSE) estimators are considered. The considered problems do not admit simple solution mainly due to the complicated constraints pertaining to the non-ideal RIS reflecting elements. As far as the LS criterion is concerned, we tackle this difficulty by first proving the optimality of orthogonal training symbols and then propose a majorization-minimization (MM)-based iterative method to design the reflection pattern, where a semi-closed form solution is obtained in each iteration. As for the LMMSE criterion, we address the joint training and reflection pattern optimization problem with an MM-based alternating algorithm, where a closed-form solution to the training symbols and a semi-closed form solution to the RIS reflecting coefficients are derived, respectively. Furthermore, an acceleration scheme is proposed to improve the convergence rate of the proposed MM algorithms. Finally, simulation results demonstrate the performance advantages of our proposed joint training and reflection pattern designs.

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.

In this paper, we consider stegotext reconstruction problem in information embedding. By adding the requirement of restoring the stegotext under certain fidelity criterion, we generalize the concept of reversible/irreversible information embedding. We focus on the stegotext reconstruction in a discrete memoryless host dependent attack channel, which can be regarded as a generalized Gel’fand-Pinsker problem with an input reconstruction constraint. For this problem, we prove an upper bound and a lower bound on its embedding capacity-distortion function, which is defined to describe the tradeoff between embedding information rate, host composition loss, and stegotext reconstruction distortion. In particular, our upper and lower bounds thus obtained match each other for the binary XOR attack channel with Hamming distortion and Costa’s additive Gaussian attack channel with quadratic loss. We further consider a variant of this problem, where host signal is available at the encoder in a causal way. For this case, we completely characterize its capacity-distortion function.

Native artificial intelligence (AI) has played a pivotal role in shaping the evolution of 6G networks. It must meet stringent real-time requirements and therefore deploying lightweight AI models is necessary. However, as wireless networks generate a multitude of data fields and only a fraction of them imposes significant impact on the AI models, it is essential to accurately identify a small amount of critical data that significantly impacts communication performance. In this paper, we propose the pervasive multi-level (PML) native AI architecture, which incorporates knowledge graph (KG) into mobile network operations to establish a wireless data KG. Leveraging the wireless data KG, we analyze the relationships among various data fields and provide the on-demand generation of minimal and effective datasets, referred to as feature datasets. Consequently, it not only enhances AI training, inference, and validation processes but also significantly reduces resource wastage and overhead for communication networks. The proposed solution includes a spatio-temporal heterogeneous graph attention neural network model (STREAM) and a feature dataset generation algorithm. Experimental results validate the exceptional capability of STREAM in handling spatio-temporal data and demonstrate that the proposed architecture effectively reduces data scale and computational costs of AI training by almost an order of magnitude.

Reconfigurable intelligent surface (RIS) technology is emerging as a promising technique for performance enhancement for next-generation wireless networks. This paper investigates the physical layer security of an RIS-assisted multiple-antenna communication system in the presence of random spatially distributed eavesdroppers. The RIS-to-ground channels are assumed to experience Rician fading. Using stochastic geometry, exact distributions of the received signal-to-noise-ratios (SNRs) at the legitimate user and the eavesdroppers located according to a Poisson point process (PPP) are derived, and closed-form expressions for the secrecy outage probability (SOP) and the ergodic secrecy capacity (ESC) are obtained to provide insightful guidelines for system design. First, the secrecy diversity order is obtained as 2/α2, where α2denotes the path loss exponent of the RIS-to-ground links. Then, it is revealed that the secrecy performance is mainly affected by the number of RIS reflecting elements,N, and the impact of the number of transmit antennas and transmit power at the base station is marginal. In addition, when the locations of the randomly located eavesdroppers are unknown, deploying the RIS closer to the legitimate user rather than to the base station is shown to be more efficient. Moreover, it is also found that the density of randomly located eavesdroppers, λe, has an additive effect on the asymptotic ESC performance given by log2(1/λe). Finally, numerical simulations are conducted to verify the accuracy of these theoretical observations.

The receiver design for multi-input multi-output (MIMO) ultra-reliable and low-latency communication (URLLC) systems can be a tough task due to the use of short channel codes and few pilot symbols. Consequently, error propagation can occur in traditional turbo receivers, leading to performance degradation. Moreover, the processing delay induced by information exchange between different modules may also be undesirable for URLLC. To address the issues, we advocate to perform joint channel estimation, detection, and decoding (JCDD) for MIMO URLLC systems encoded by short low-density parity-check (LDPC) codes. Specifically, we develop two novel JCDD problem formulations based on the maximuma posteriori(MAP) criterion for Gaussian MIMO channels and sparse mmWave MIMO channels, respectively, which integrate the pilots, the bit-to-symbol mapping, the LDPC code constraints, as well as the channel statistical information. Both the challenging large-scale non-convex problems are then solved based on the alternating direction method of multipliers (ADMM) algorithms, where closed-form solutions are achieved in each ADMM iteration. Furthermore, two JCDD neural networks, called JCDDNet-G and JCDDNet-S, are built by unfolding the derived ADMM algorithms and introducing trainable parameters. It is interesting to find via simulations that the proposed trainable JCDD receivers can outperform the turbo receivers with affordable computational complexities.

Channel state information (CSI) compression and feedback is a common way of acquiring the CSI at the transmitter in frequency division duplex (FDD) massive multiple-input multiple-output (mMIMO) systems due to the lack of channel reciprocity. However, implicit reciprocity potentially exists in the bi-directional channels of an FDD system because they in fact share physically the same propagation paths. We propose to leverage this implicit reciprocity in FDD mMIMO systems to minimize the feedback overhead and enhance the CSI recovery with uplink channel information at the transmitter. To achieve this, we develop a disentangled representation (DR) learning enabled neural network (NN), named DrCsiNet, to realize the selective CSI compression feedback with the assistance of uplink CSI. The proposed DrCsiNet successfully extracts the information of reciprocity implicitly shared between the downlink and uplink channels in FDD mMIMO, while it simultaneously extracts selective information from the downlink CSI excluding the implicit reciprocity component for compression feedback. We conduct extensive simulations to evaluate the performance of the proposed DrCsiNet against existing methods under various setups. Results demonstrate remarkable performance gains of DrCsiNet for CSI recovery and evidence a strong generalization ability across various network structures. These findings validate the efficacy of disentangling implicit CSI reciprocity embedded in uplink CSI for enhancing the downlink CSI recovery in FDD mMIMO.

This paper evaluates the geometrically averaged performance of a wireless communication network assisted by a multitude of distributed reconfigurable intelligent surfaces (RISs), where the RIS locations are randomly dropped obeying a homogeneous Poisson point process. By exploiting stochastic geometry and then averaging over the random locations of RISs as well as the serving user, we first derive a closed-form expression for the spatially ergodic rate in the presence of phase errors at the RISs in practice. Armed with this closed-form characterization, we then optimize the RIS deployment under a reasonable and fair constraint of a total number of RIS elements per unit area. The optimal configurations in terms of key network parameters, including the RIS deployment density and the array sizes of RISs, are disclosed for the spatially ergodic rate maximization. Our findings suggest that deploying larger-size RISs with reduced deployment density is theoretically preferred to support extended RIS coverages, under the cases of bounded phase shift errors. However, when dealing with random phase shifts, the reflecting elements are recommended to spread out as much as possible, disregarding the deployment cost.Furthermore, the spatially ergodic rate loss due to the phase shift errors is quantitatively characterized. For bounded phase shift errors, the rate loss is eventually upper bounded by a constant as$N\rightarrow \infty $, where N is the number of reflecting elements at each RIS. While for random phase shifts, this rate loss scales up in the order of$\log N$. These analytical observations are validated through numerical results.

This paper investigates the integration of a reconfigurable intelligent surface (RIS) into a secure multiuser massive multiple-input multiple-output (MIMO) system in the presence of transceiver hardware impairments (HWI), imperfect channel state information (CSI), and spatially correlated channels. We first introduce a linear minimum-mean-square error estimation algorithm for the aggregate channel by considering the impact of transceiver HWI and RIS phase-shift errors. Then, we derive a lower bound for the achievable ergodic secrecy rate in the presence of a multi-antenna eavesdropper when artificial noise (AN) is employed at the base station (BS). In addition, the obtained expressions of the ergodic secrecy rate are further simplified in some noteworthy special cases to obtain valuable insights. To counteract the effects of HWI, we present a power allocation optimization strategy between the confidential signals and AN, which admits a fixed-point equation solution. Our analysis reveals that a non-zero ergodic secrecy rate is preserved if the total transmit power decreases no faster than 1/N, whereNis the number of RIS elements. Moreover, the ergodic secrecy rate grows logarithmically with the number of BS antennasMand approaches a certain limit in the asymptotic regimeN→ ∞. Simulation results are provided to verify the derived analytical results. They reveal the impact of key design parameters on the secrecy rate. It is shown that, with the proposed power allocation strategy, the secrecy rate loss due to HWI can be counteracted by increasing the number of low-cost RIS elements.

Reconfigurable intelligent surface (RIS) is regarded as an important enabling technology for the sixth-generation (6G) network. Recently, modulating information in reflection patterns of RIS, referred to as reflection modulation (RM), has been proven in theory to have the potential of achieving higher transmission rate than existing passive beamforming (PBF) schemes of RIS. To fully unlock this potential of RM, we propose a novel superimposed RIS-phase modulation (SRPM) scheme for multiple-input multiple-output (MIMO) systems, where tunable phase offsets are superimposed onto predetermined RIS phases to bear extra information messages. The proposed SRPM establishes a universal framework for RM, which retrieves various existing RM-based schemes as special cases.Moreover, the advantages and applicability of the SRPM in practice is also validated in theory by analytical characterization of its performance in terms of average bit error rate (ABER) and ergodic capacity. To maximize the performance gain, we formulate a general precoding optimization at the base station (BS) for a single-stream case with uncorrelated channels and obtain the optimal SRPM design via the semidefinite relaxation (SDR) technique. Furthermore, to avoid extremely high complexity in maximum likelihood (ML) detection for the SRPM, we propose a sphere decoding (SD)-based layered detection method with near-ML performance and much lower complexity. Numerical results demonstrate the effectiveness of SRPM, precoding optimization, and detection design. It is verified that the proposed SRPM achieves a higher diversity order than that of existing RM-based schemes and outperforms PBF significantly especially when the transmitter is equipped with limited radio-frequency (RF) chains.

To enable wireless federated learning (FL) in communication resource-constrained networks, two communication schemes, i.e., digital and analog ones, are effective solutions. In this paper, we quantitatively compare these two techniques, highlighting their essential differences as well as respectively suitable scenarios. We first examine both digital and analog transmission schemes, together with a unified and fair comparison framework under imbalanced device sampling, strict latency targets, and transmit power constraints. A universal convergence analysis under various imperfections is established for evaluating the performance of FL over wireless networks. These analytical results reveal that the fundamental difference between the digital and analog communications lies in whether communication and computation are jointly designed or not. The digital scheme decouples the communication design from FL computing tasks, making it difficult to support uplink transmission from massive devices with limited bandwidth and hence the performance is mainly communication-limited. In contrast, the analog communication allows over-the-air computation (AirComp) and achieves better spectrum utilization. However, the computation-oriented analog transmission reduces power efficiency, and its performance is sensitive to computation errors from imperfect channel state information (CSI). Furthermore, device sampling for both schemes are optimized and differences in sampling optimization are analyzed. Numerical results verify the theoretical analysis and affirm the superior performance of the sampling optimization.

In the realm of industrial Internet of Things, the imperative for ultra-reliable and low-latency communication (URLLC) is underscored by the demand for up to 99.999% reliability and sub-microsecond latency. In this paper, we delve into a downlink cell-free massive multiple-input multiple-output (MIMO) system designed to facilitate URLLC, operating over spatially correlated Rician fading channels with inherent phase shifts. Utilizing short-packet transmission and accounting for imperfect channel state information, we derive stringent closed-form expressions for the lower-bound achievable rates, considering both phase-aware and phase-unaware minimum mean squared error estimations. Employing these expressions, we execute an in-depth performance analysis across diverse system configurations, including the availability of phase shifts and the counts of access points (APs), connected devices, antennas per AP, and pilot sequences. Additionally, we propose a path-following power control algorithm that employs geometric programming to enhance the downlink sum-rate. This algorithm is meticulously designed to meet the stringent latency and reliability requirements of URLLC for all connected devices. The theoretical underpinnings and the efficacy of the proposed power control algorithm are substantiated through extensive simulations.

A novel joint phase noise compensation and signal detection algorithm based on deep learning (DL) is proposed for orthogonal frequency division multiplexing (OFDM) systems under rapidly time-varying channels. Specifically, we first develop an alternating direction method of multipliers (ADMM) based solution to the difficult mixed-integer maximum likelihood (ML) estimation, along with a low-complexity ADMM solution that exploits the inherent feature of the inter-carrier interference (ICI). Furthermore, by unfolding the ADMM iterations, we present a model-driven network with trainable variables, which avoids cumbersome parameter search and also results in faster convergence. Simulation results demonstrate that our proposed DL-aided ADMM algorithms significantly outperform existing baselines in terms of BER performance with acceptable complexity.

We study the resource allocation for an uplink ultra reliable and low-latency communication (URLLC) system. The receive beamformer at the base station (BS) and the transmit power of users are jointly optimized to minimize the finite block-length (FBL) decoding error probability. To solve the difficult nonconvex problem, we first acquire a tractable reformulation by approximating the Gaussian Q-function. Although the resultant problem is still nonconvex, we propose an efficient algorithm where the optimal receive beamformer is obtained in closed form and the transmit powers of users are optimized by employing the majorization-minimization (MM) technique. Simulation results and complexity analysis verify the superiority of the proposed algorithm over existing benchmark schemes.

Interferences between uplink communication and downlink radar sensing can severely impair the performance of the time-division duplex (TDD) multiple-input-multiple-output (MIMO) integrated sensing and communication (ISAC) system. In this paper, we propose an efficient joint downlink and uplink design to address this issue. Specifically, we jointly optimize the downlink radar sensing waveform and the uplink power allocation to minimize the weighted sum of sensing and communication mean squared errors (MSEs). Despite the non-convexity of the joint downlink and uplink design problem, we develop a majorization-minimization (MM) based algorithm to determine an efficient solution. The proposed algorithm is proved to be convergent. Moreover, as validated via simulation results, the proposed joint design can effectively mitigate the interference between uplink communication and radar sensing and provide remarkable performance gains over conventional separate design.

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.

The problem of joint information and noisy Gaussian state amplification is investigated in this paper. The optimal capacity-distortion tradeoff is characterized. For the achievability part, the Gelfand-Pinsker scheme is evaluated using minimum mean squared error of Gaussian random variables. For the converse part, Cauchy-Schwartz inequality is invoked to transform the optimal linear estimation into a canonical form.

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.

In this article, we consider an unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) and relay communication network consisting of a base station (BS), a UAV, and multiple ground users. The UAV acts as both a wireless power transmission source and an uplink communication relay. Specifically, an entire transmission period of the considered system is divided into two stages. In the first stage, the UAV transfers the power to the ground users along a well-optimized flight trajectory and meanwhile, the users transmit data to the UAV using the harvested energy. Subsequently, in the second stage, the UAV flies to the vicinity of the BS and forwards the data to the BS. For the purpose of minimizing the energy consumed by the UAV, we jointly optimize the time durations of the two stages, the UAV’s transmit powers for WPT and data forwarding, as well as its flight trajectory, subject to the constraints of the Quality of Service (QoS), the information forwarding, the energy causality, and the mobility of the UAV. The involved optimization problem is nonconvex and highly intractable. To this end, we propose an efficient alternating algorithm to iteratively solve the two subproblems with respect to the time durations of the two stages and the UAV’s transmit powers and trajectory, respectively. The first subproblem has a closed-form optimal solution and the second subproblem is handled by addressing a surrogate convex problem based on the technique of successive convex approximation. Finally, the simulation results confirm the superiority of our proposed algorithm.

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.

In optical wireless communications (OWC), hybrid optical orthogonal frequency division multiplexing (O-OFDM) schemes, such as hybrid asymmetrically clipped O-OFDM (HACO-OFDM) and layered asymmetrically clipped O-OFDM (LACO-OFDM), enjoy both high power and spectral efficiency. However, due to the non-orthogonal transmission of signal components induced by the clipping distortion, successive interference cancellation (SIC) is required in these hybrid O-OFDM schemes, leading to notably increased complexity. In this paper, we conceive novel hybrid O-OFDM schemes to offer both high spectral and power efficiency, whilst possessing low complexity. By elaborately designing a time-domain (TD) distortion elimination methodology at the transmitter, a novel distortion-elimination hybrid O-OFDM (DEHO-OFDM) is first proposed, which combines asymmetrically clipped O-OFDM (ACO-OFDM) and pulse-amplitude-modulated discrete multitone (PAM-DMT) in an interference-free manner. In order to further improve the spectral efficiency, an enhance DEHO-OFDM (EDEHO-OFDM) is designed by activating the remaining subcarrier resources. Both DEHO-OFDM and EDEHO-OFDM can be realized through a single-IFFT transmitter and standard OFDM receiver, leading to much lower complexity than the existing hybrid O-OFDM schemes. Simulation results have demonstrated the superiorities of the proposed schemes over HACO-OFDM and LACO-OFDM in terms of peak-to-average-power ratio (PAPR) and bit error rate (BER).

In this paper, we investigate the design of statistically robust detectors for multi-input multi-output (MIMO) systems subject to imperfect channel state information (CSI). A robust maximum likelihood (ML) detection problem is formulated by taking into consideration the CSI uncertainties caused by both the channel estimation error and the channel variation. To address the challenging discrete optimization problem, we propose an efficient alternating direction method of multipliers (ADMM)-based algorithm, which only requires calculating closed-form solutions in each iteration. Furthermore, a robust detection network RADMMNet is constructed by unfolding the ADMM iterations and employing both model-driven and data-driven philosophies. Moreover, in order to relieve the computational burden, a low-complexity ADMM-based robust detector is developed using the Gaussian approximation, and the corresponding deep unfolding network LCRADMMNet is further established. On the other hand, we also provide a novel robust data-aided Kalman filter (RDAKF)-based channel tracking method, which can effectively refine the CSI accuracy and improve the performance of the proposed robust detectors. Simulation results validate the significant performance advantages of the proposed robust detection networks over the non-robust detectors with different CSI acquisition methods.

In this paper, a new randomized iterative detection algorithm (NRIDA) is proposed for uplink large-scale MIMO systems, where the random iterations in it are designed to work for the detection model (denoted by$\mathbf {y}=\mathbf {Hx}+\mathbf {n}$) directly. Different from those traditional iterations designed for the linear system (denoted by$\mathbf {Ax}=\mathbf {b}$with$\mathbf {A}=\mathbf {H}^{H}\mathbf {H}$and$\mathbf {b}=\mathbf {H}^{H}\mathbf {y}$), we show that besides the complexity reduction about the matrix inversion, in the proposed NRIDA the computational complexity of matrix multiplication for the linear detection is also greatly reduced without any performance loss, thus leading to a much lower detection complexity. Meanwhile, according to convergence analysis, we demonstrate that the proposed NRIDA enjoys a globally exponential convergence performance, enabling it well suited to the various detection cases of interest. Besides, further complexity reduction and the choices of the sampling distribution in NRIDA are studied as well in full details. Moreover, in order to achieve a better detection trade-off between performance and complexity, we introduce the concept of the conditional sampling into NRIDA, which brings significant gains in both iteration convergence and efficiency. Finally, simulations with respect to the uplink large-scale MIMO detection are presented to illustrate the remarkable gains of the proposed NRIDA in both performance and complexity.

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.

It is supposed that ultra-reliable low latency communication (uRLLC) would continue to evolve in the future sixth generation (6G) network, to provide enhanced capability towards extreme connectivity, with the aid of well established multiple-input multiple-output (MIMO) technology. Since the latency constraint can be represented equivalently by the blocklength of a codeword, channel coding theory at a finite blocklength plays an important role in theoretic analysis of uRLLC. Based on Polyanskiy’s and Yang’s asymptotic results on maximal achievable rate, we first derive the proximate closed-form expressions for the expectation and variance of channel dispersion. Then, the upper bound of average maximal achievable rate is obtained for massive MIMO systems under ideal independent and identically distributed fading channels. Since almost all the fundamental parameters, including the spatial degree-of-freedom (DoF), are considered, this expression can be viewed as a performance bound of the spatiotemporal two-dimension channel coding to some extent. Moreover, it is shown by simulation and analysis, as the DoF goes to infinity, MIMO systems reveal a nature of deterministic transmission, since the average maximal achievable coding rate per antenna can be achieved at each transmission. In this case, the inversely proportional law observed therein implies that the blocklength in the time domain can be further shortened at the expense of spatial DoF. This exchangeability of space and time, to support a given coding rate, paves a solid and feasible road for us to further reduce latency in 6G uRLLC.

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 the uplink performance of a general cell-free massive multiple-input multiple-output (CF-mMIMO) system, in which all access points (APs) and user equipments (UEs) suffer from hardware impairments (HWIs). Besides, there are several reconfigurable intelligent surfaces (RISs) that aim to improve the coverage quality, spectral efficiency (SE), and energy efficiency (EE). Relying on the knowledge of only imperfect channel state information, a tight closed-form expression for the lower-bound achievable SE is derived. Based on this expression, we quantitatively investigate the impacts of different system parameters on uplink SE and EE, and conduct a tradeoff analysis between using more APs versus using more RISs with respect to the above performance metrics. In addition, we also design a max-min SE algorithm that takes into account both large-scale fading decoding weights and power control coefficients to guarantee UE fairness. Specifically, the proposed algorithm admits a closed-form solution and is therefore memory-efficient and time-saving. Both the theoretical analysis and the effectiveness of the proposed max-min SE algorithm are verified via extensive simulations.

Reconfigurable intelligent surfaces (RISs) can establish favorable wireless environments to combat the severe attenuation and blockages in millimeter-wave (mmWave) bands. However, to achieve the optimal enhancement of performance, the instantaneous channel state information (CSI) needs to be estimated at the cost of a large overhead that scales with the number of RIS elements and the number of users. In this paper, we design a quasi-static broad coverage at the RIS with the reduced overhead based on the statistical CSI. We propose a design framework to synthesize the power pattern reflected by the RIS that meets the customized requirements of broad coverage. For the communication of broadcast channels, we generalize the broad coverage of the single transmit stream to the scenario of multiple streams. Moreover, we employ the quasi-static broad coverage for a multiuser orthogonal frequency division multiplexing access (OFDMA) system, and derive the analytical expression of the downlink rate, which is proved to increase logarithmically with the power gain reflected by the RIS. By taking into account the overhead of channel estimation, the proposed quasi-static broad coverage even outperforms the design method that optimizes the RIS phases using the instantaneous CSI. Numerical simulations are conducted to verify these observations.

The trend of developing distributed multiple-input multiple-output (MIMO) cooperation has been growing for future wireless networks due to its potential of capacity improvement through network-level precoding. In massive MIMO applications, the overhead of channel state information (CSI) exchange among distributed transmitters is too large to make it possible in practical implementations. In this paper, we consider a cooperative multicell massive MIMO network with distributed regularized zero-forcing (RZF) precoding at each base station (BS), where a novel CSI exchange scheme is devised to reduce the interactive overhead. As a key finding of this work, we theoretically prove that it suffices to share the Gram matrix of local CSI among the cooperative BSs in order to achieve the same performance as a centralized cooperative MIMO network using the RZF precoding with global CSI sharing. The CSI exchange from each BS is thus reduced to a symmetric matrix that has a much smaller size than the full CSI and the amount of CSI exchange does NOT grow with the large number of antennas in massive MIMO. Specifically, based on the exchanged Gram matrices, we derive a decentralized RZF precoding design at each BS and develop both the optimal and suboptimal cooperative power allocation strategies, which achieve different performance and complexity tradeoffs. A virtual centralized power allocation is accomplished at each BS and the performance achieved by the proposed decentralized precoding is the same as the centralized benchmark scheme with full CSI exchange. These superiorities of the proposed schemes are verified through simulation results.

To meet the escalating demand on spectral resource in massive machine-type communication (mMTC) applications, a critical solution is applying massive grant-free transmission to the millimeter-wave (mmWave) band. In this paper, to maximize the ergodic rate, we propose an efficient hybrid analog/digital beamforming (HBF) design algorithm for the massive grant-free transmission in uplink mmWave systems. Specifically, to make the HBF design problem tractable, we first leverage the deterministic equivalent method to derive an approximate expression of the ergodic rate for the mMTC in the mmWave system. Since the ergodic rate maximization-based HBF design problem is nonconvex, we leverage the alternating optimization strategy and propose a semidefinite relaxation-based HBF algorithm to improve the ergodic rate. Simulation results verify the superior performance of the proposed HBF design algorithm in improving the ergodic rate.

In this article, the performance of simultaneous wireless information and power transfer (SWIPT) in downlink (DL) Internet of Things (IoT) networks relying on the cell-free massive multiple-input–multiple-output (CF-mMIMO) technique is investigated. In such a network, the access points (APs) beam the radio-frequency (RF) energy toward IoT sensors during the DL wireless power transfer phase. Tight closed-form expressions for DL harvested energy (HE) and achievable rate with conjugate beamforming (CB) and normalized CB (NCB) are, respectively, derived, which enable us to analyze the behaviors of CB and NCB schemes in terms of both HE and achievable rate. Apart from this, to guarantee sensor fairness with respect to the HE and achievable rate, a max–min power control strategy based on the accelerated projected gradient (APG) method is proposed. Specifically, the proposed APG-based power control is able to determine the optimal solution in closed form and is more memory efficient than the convex-solver-based counterpart. These analytical results as well as the effectiveness of the proposed power control policy are verified by experimental simulations.

This paper investigates the power allocation problem for secure multiple-input single-output transmission with the injection of artificial noise (AN), in the presence of an unknown eavesdropper (Eve). Two power allocation schemes, the optimal adaptive power allocation (OAPA) and suboptimal fixed power allocation (SFPA) schemes, are proposed to enhance the physical layer security of the considered system. Since the noise power at Eve is unknown, both power allocation schemes are designed for the worst-case scenario in which the noise power at Eve is assumed to be zero, aiming to minimize the secrecy outage probability (SOP). To characterize the performance of the proposed power allocation schemes, approximate closed-form expressions for average SOP under a preset noise power level are derived by applying Gauss-Chebyshev quadrature. We also address the worst-case secrecy outage performance for the proposed OAPA and SFPA schemes. Our analytical and numerical results show that, compared with the exhaustive search method that requires Eve’s prior information, the proposed OAPA scheme exhibits comparable secrecy outage performance without Eve’s prior information. Additionally, the SFPA scheme, also without Eve’s prior information, is capable of achieving almost the same worst-case SOP as the OAPA scheme, with a much lower implementation complexity.

Channel reciprocity-based key generation (CRKG) has recently emerged as a new technique to address the problem of key distribution in wireless networks. However, as this approach relies upon the characteristics of fading channels, the corresponding secret key rate may be low when the communication link is blocked. To enhance the applicability of CRKG in harsh propagation scenarios, this paper introduces a novel multiuser key generation scheme, which is referred to as RIS-assisted multiuser key generation (RMK) that leverages the reconfigurable intelligent surface (RIS) technology for appropriately shaping the environment and enhancing the sum secret key rate between an access point and multiple users. In the RMK scheme, an RIS-induced channel, rather than the direct channel, serves as the key source. We derive a general closed-form expression of the secret key rate and optimize the configuration of the RIS to maximize the sum secret key rate over independent and correlated fading channels in the presence of multiple users. In the presence of independent fading, we introduce a low-complexity algorithm based on the Karush-Kuhn-Tucker (KKT) condition. In the presence of correlated fading, the optimization problem is non-convex and challenging to solve. To tackle it, we propose a new optimization algorithm based on the semi-definite relaxation (SDR) and successive convex approximation (SCA) methods. Simulation results demonstrate that the proposed RMK scheme outperforms existing RIS-assisted algorithms and achieves a near-optimal sum secret key rate over independent and correlated fading channels.

In this paper, we consider a platform of flying mobile edge computing (F-MEC), where unmanned aerial vehicles (UAVs) serve as equipment providing computation resource, and they enable task offloading from user equipment (UE). We aim to minimize energy consumption of all UEs via optimizing user association, resource allocation and the trajectory of UAVs. To this end, we first propose a Convex optimizAtion based Trajectory control algorithm (CAT), which solves the problem in an iterative way by using block coordinate descent (BCD) method. Then, to make the real-time decision while taking into account the dynamics of the environment (i.e., UAV may take off from different locations), we propose a deep Reinforcement leArning based trajectory control algorithm (RAT). In RAT, we apply the Prioritized Experience Replay (PER) to improve the convergence of the training procedure. Different from the convex optimization based algorithm which may be susceptible to the initial points and requires iterations, RAT can be adapted to any taking off points of the UAVs and can obtain the solution more rapidly than CAT once training process has been completed. Simulation results show that the proposed CAT and RAT achieve the considerable performance and both outperform traditional algorithms.

In this paper, the problem of maximizing the wireless users’ sum-rate for uplink rate splitting multiple access (RSMA) communications is studied. In the considered model, the message intended for a single user is split into two sub-messages with separate transmit power and the base station (BS) uses a successive decoding technique to decode the received messages. To maximize each user’s transmission rate, the users must adjust their transmit power and the BS must determine the decoding order of the messages transmitted from the users to the BS. This problem is formulated as a sum-rate maximization problem with proportional rate constraints by adjusting the users’ transmit power and the BS’s decoding order. However, since the decoding order variable in the optimization problem is discrete, the original maximization problem with transmit power and decoding order variables can be transformed into a problem with only the rate splitting variable. Then, the optimal rate splitting of each user is determined. Given the optimal rate splitting of each user and a decoding order, the optimal transmit power of each user is calculated. Next, the optimal decoding order is determined by an exhaustive search method. To further reduce the complexity of the optimization algorithm used for sum-rate maximization in RSMA, a user pairing based algorithm is introduced, which enables two users to use RSMA in each pair and also enables the users in different pairs to be allocated with orthogonal frequency. For comparisons, the optimal sum-rate maximizing solutions with proportional rate constraints are obtained for non-orthogonal multiple access (NOMA), frequency division multiple access (FDMA), and time division multiple access (TDMA). Simulation results show that RSMA can achieve up to 10.0, 22.2, and 81.2 percent gains in terms of sum-rate compared to NOMA, FDMA, and TDMA.

This paper investigates the problem of resource allocation for a wireless communication network with distributed reconfigurable intelligent surfaces (RISs). In this network, multiple RISs are spatially distributed to serve wireless users and the energy efficiency of the network is maximized by dynamically controlling the on-off status of each RIS as well as optimizing the reflection coefficients matrix of the RISs. This problem is posed as a joint optimization problem of transmit beamforming and RIS control, whose goal is to maximize the energy efficiency under minimum rate constraints of the users. To solve this problem, two iterative algorithms are proposed for the single-user case and multi-user case. For the single-user case, the phase optimization problem is solved by using a successive convex approximation method, which admits a closed-form solution at each step. Moreover, the optimal RIS on-off status is obtained by using the dual method. For the multi-user case, a low-complexity greedy searching method is proposed to solve the RIS on-off optimization problem. Simulation results show that the proposed scheme achieves up to 33% and 68% gains in terms of the energy efficiency in both single-user and multi-user cases compared to the conventional RIS scheme and amplify-and-forward relay scheme, respectively.

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.

We propose a UAV-relayed covert communication scheme with finite blocklength to maximize the effective transmission rate from the transmitter to the legitimate receiver against a flying warden. The transmitter adopts the maximum ratio transmission and the relay performs Gaussian signaling transmission to cause uncertainty at the warden. First, the optimal detection thresholds are derived at the warden towards the transmitter and the relay, respectively. Then, the hovering location of the warden is optimized to maximize the summation of relative entropies from the transmitter and the relay, which can greatly threaten the covertness. With this worst covert situation, the blocklength and transmit power at the transmitter and the relay are jointly optimized under the constraint of the error detection probability to maximize the effective transmission rate from the transmitter to the legitimate receiver. Numerical results are provided to demonstrate the effectiveness of the proposed UAV-relayed covert communication scheme.

In this paper, the problem of optimal passive beamforming design is studied for a reconfigurable intelligent surface (RIS) assisted full-duplex (FD) communication system. In the studied model, two devices communicate with each other using one RIS under the FD mode. Each of the device will receive not only the message from the other device but also the self-interference. The main problem of this work is to minimize the sum transmit power by jointly optimizing the reflection coefficients matrix and the transmit power of devices. To solve this problem, a dual method is proposed, where the dual problem is formulated as a semidefinite programming problem. After solving the dual problem, the phase beamforming of the RIS is obtained in the closed form. Simulation results show that the proposed scheme can reduce up to 66% sum transmit power compared to a conventional RIS assisted half-duplex mode.

In this paper, the implementation of a distributed primal-dual learning algorithm over realistic wireless networks is investigated. In the considered model, the users and one base station (BS) cooperatively perform a distributed primal-dual learning algorithm for controlling and optimizing wireless networks. In particular, each user must locally update the primal and dual variables and send the updated primal variables to the BS. The BS aggregates the received primal variables and broadcasts the aggregated variables to all users. Since all of the primal and dual variables as well as aggregated variables are transmitted over wireless links, the imperfect wireless links will affect the solution achieved by the distributed primal-dual algorithm. Therefore, it is necessary to study how wireless factors such as transmission errors affect the implementation of the distributed primal-dual algorithm and how to optimize wireless network performance to improve the solution achieved by the distributed primal-dual algorithm. To address these challenges, the convergence rate of the primal-dual algorithm is provided in a closed form while considering the impact of wireless factors such as data transmission errors. Simulation results show that the proposed distributed primal-dual algorithm can reduce the gap between the target and obtained solution compared to the distributed primal-dual learning algorithm without considering imperfect wireless transmission.

Owing to the ever increasing of information privacy requirement, covert communication has gained more and more attention, whose effective range is limited by the trade-off between the covertness and the transmit power. Benefiting from the high mobility and easy deployment, unmanned aerial vehicles (UAVs) can be utilized to expand the range of covert networks. Thus, we propose a UAV-relayed covert communication scheme with finite blocklength to maximize the effective transmission bits from the transmitter to the legitimate receiver against a flying warden. The transmitter adopts the maximum ratio transmission and the relay performs Gaussian signalling transmission to cause uncertainty at the warden. First, the optimal detection thresholds are derived at the warden towards the transmitter and the relay, respectively. Then, the hovering location of the warden is optimized to maximize the summation of relative entropies from the transmitter and the relay, which can greatly threaten the covertness. With this worst covert situation, the blocklength and transmit power at the transmitter and the relay are jointly optimized under the constraint of the end-to-end error detection probability to maximize the effective transmission rate from the transmitter to the legitimate receiver. Numerical results are provided to demonstrate the effectiveness of the proposed UAV-relayed covert communication scheme.

Coarse quantization by using low-resolution analog-to-digital converters (ADCs) is an attractive approach to relieve the burden of power consumption and hardware cost of implementing massive multiple-input multiple-output (MIMO) systems. In this article, we analyze the uplink spectral efficiency of a multiuser massive MIMO system with low-resolution ADCs in the context of orthogonal frequency division multiplexing (OFDM) under multipath channels. Firstly, we develop an efficient pilot scheme which results in a constant average power of the quantization noise for different channel delay power spectrums and also minimizes the mean squared error of channel estimation. Then a tight approximation of the uplink achievable rate is derived in a closed form considering both perfect channel state information (CSI) and estimated CSI. Then we analyze the impact of multipath channels on the system performance. Under perfect CSI, we discover that an increment of multipath taps has a positive impact on compensating the performance degradation due to the quantization noise. Under imperfect CSI, the most beneficial channel is uniformly distributed over a specific number of taps. Simulations are conducted to verify our analytical results.

We investigate massive access in the Internet-of-Things (IoT) relying on multi-pair two-way amplify-and-forward (AF) relay systems using massive multiple-input multiple-output (MIMO). We utilize the approximate message passing (AMP) algorithm for joint device activity detection and channel estimation. Furthermore, we analyze the achievable rates for multiple pairs of active devices and derive the closed-form expressions for both maximum-ratio combining/maximum-ratio transmission (MRC/MRT) and zero-forcing reception/zero-forcing transmission (ZFR/ZFT)-based beamforming schemes adopted at the relay. Moreover, to improve the achievable sum rates, we propose a low-complexity algorithm for optimizing the pilot length L. Our simulation results verify the accuracy of the closed-form expressions of the MRC/MRT and ZFR/ZFT scenarios. Finally, the proposed pilot-length optimization algorithm performs well in both the MRC/MRT and ZFR/ZFT scenarios.

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.

In this paper, we study the average packet error probability (APEP) and effective throughput (ET) of the control link in unmanned-aerial-vehicle (UAV) communications, where the ground central station (GCS) sends control signals to the UAV that requires ultra-reliable and low-latency communications (URLLC). To ensure the low latency, short packets are adopted for the control signal. As a result, the Shannon capacity theorem cannot be adopted here due to its assumption of infinite channel blocklength. We consider both free space (FS) and 3-Dimensional (3D) channel models by assuming that the locations of the UAV are randomly distributed within a restricted space. We first characterize the statistical characteristics of the signal-to-noise ratio (SNR) for both FS and 3D models. Then, the closed-form analytical expressions of APEP and ET are derived by using Gaussian-Chebyshev quadrature. Also, the lower bounds are derived to obtain more insights. Finally, we obtain the optimal value of packet length with the objective of maximizing the ET by applying one-dimensional search. Our analytical results are verified by the Monte-Carlo simulations.

This article investigates the problem of resource allocation for multiuser communication networks with a reconfigurable intelligent surface (RIS)-assisted wireless transmitter. In this network, the sum transmit power of the network is minimized by controlling the phase beamforming of the RIS and transmit power of the base station. This problem is posed as a joint optimization problem of transmit power and RIS control, whose goal is to minimize the sum transmit power under signal-to-interference-plus-noise ratio (SINR) constraints of the users. To solve this problem, a dual method is proposed, where the dual problem is obtained as a semidefinite programming problem. After solving the dual problem, the phase beamforming of the RIS is obtained in the closed form, while the optimal transmit power is obtained by using the standard interference function. Simulation results show that the proposed scheme can reduce up to 94% and 27% sum transmit power compared to the maximum ratio transmission (MRT) beamforming and zero-forcing (ZF) beamforming techniques, respectively.

In practical implementations, physical-layer key generation (PKG) encounters the bottlenecks of imperfect channel reciprocity, nearby attack, and high temporal auto-correlation. Existing One-Band Multiple-Antenna Loop-bAck key generation (OB-MALA) schemes try to address these challenges through establishing bi-directional channels via echoing rotated received signals. However, we find that OB-MALA schemes can be vulnerable to a multiply-divide (MD) attack, as they echo the received signals through the same band with the pilot signals. To overcome this deficiency, we propose a new method, named Two-Band Multiple-Antenna Loop-bAck key generation (TB-MALA), which exploits two separate bands for pilot transmission and echo reception. The TB-MALA is proved to be robust to the imperfect channel reciprocity caused by radio frequency (RF) front-ends and can resist both the nearby attack and the MD attack. It also reduces the auto-correlation of effective channels with the help of a rotation matrix. The secret key rate of TB-MALA is analyzed and the closed-form of a lower bound is derived for the worst case. Numerical results demonstrate that the proposed TB-MALA protects against these attacks and achieves performance comparable to the ideal case with the perfect reciprocity of RF front-ends. It can thus be used to form a robust, fast, and secure key generation in a multiple-input and multiple-output (MIMO) system.

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.

This paper investigates the problem of resource allocation for a wireless communication network with distributed reconfigurable intelligent surfaces (RISs). In this network, multiple RISs are spatially distributed to serve wireless users and the energy efficiency of the network is maximized by dynamically controlling the on-off status of each RIS as well as optimizing the reflection coefficient matrix of the RISs. This problem is posed as a joint optimization problem of transmit power and RIS control, whose goal is to maximize the energy efficiency under minimum rate constraints of the users. To solve this problem, an alternating algorithm is proposed by solving two sub-problems iteratively. The phase optimization sub-problem is solved by using a successive convex approximation method, which admits a closed-form solution at each step. Moreover, the RIS on-off optimization sub-problem is solved by using the dual method. Simulation results show that the proposed scheme achieves up to 27% and 68% gains in terms of the energy efficiency compared to the conventional RIS scheme and amplify-and-forward relay scheme, respectively.

In a multiuser massive multiple-input multipleoutput (MIMO) system, hybrid analog-and-digital structure is widely applied due to the high cost of deploying a large number of radio-frequency (RF) chains to drive the large antenna array. Phase shifter network is a common way of accomplishing the analog component. However, phase shifters impaired by hardware constrains can seriously degrade the performance of the system. This paper investigates the influence of imperfect phase shifters on the uplink achievable rate of the system with fullyconnected and sub-connected architectures. We derive a tractable expression for the uplink achievable sum rate. The proposed studies show that massive antennas are able to compensate for the performance degradation caused by imperfect phase shifters in the hybrid massive MIMO system. Our analytical results are verified by extensive simulations.

Unmanned aerial vehicle (UAV)-assisted communication is a promising technology in future wireless communication networks. UAVs can not only help offload data traffic from ground base stations (GBSs) but also improve the Quality of Service (QoS) of cell-edge users (CEUs). In this article, we consider the enhancement of cell-edge communications through a mobile relay, i.e., UAV, in multicell networks. During each transmission period, GBSs first send data to the UAV, and then the UAV forwards its received data to CEUs according to a certain association strategy. In order to maximize the sum rate of all CEUs, we jointly optimize the UAV mobility management, including trajectory, velocity, and acceleration, and association strategy of CEUs to the UAV, subject to minimum rate requirements of CEUs, mobility constraints of the UAV, and causal buffer constraints in practice. To address the mixed-integer nonconvex problem, we transform it into two convex subproblems by applying tight bounds and relaxations. An iterative algorithm is proposed to solve the two subproblems in an alternating manner. Numerical results show that the proposed algorithm achieves higher rates of CEUs as compared with the existing benchmark schemes.

In this article, we consider a hybrid mobile edge computing (H-MEC) platform, which includes ground stations (GSs), ground vehicles (GVs), and unmanned aerial vehicles (UAVs), all with the mobile edge cloud installed to enable user equipments (UEs) or Internet of Things (IoT) devices with intensive computing tasks to offload. Our objective is to obtain an online offloading algorithm to minimize the energy consumption of all the UEs, by jointly optimizing the positions of GVs and UAVs, user association and resource allocation in real time, while considering the dynamic environment. To this end, we propose a hybrid deep-learning-based online offloading (H2O) framework where a large-scale path-loss fuzzy c-means (LS-FCM) algorithm is first proposed and used to predict the optimal positions of GVs and UAVs. Second, a fuzzy membership matrix U-based particle swarm optimization (U-PSO) algorithm is applied to solve the mixed-integer nonlinear programming (MINLP) problems and generate the sample data sets for the deep neural network (DNN) where the fuzzy membership matrix can capture the small-scale fading effects and the information of mutual interference. Third, a DNN with the scheduling layer is introduced to provide the user association and computing resource allocation under the practical latency requirement of the tasks and limited available computing resource of H-MEC. In addition, different from the traditional DNN predictor, we only input one UE's information to the DNN at one time, which will be suitable for the scenarios where the number of UE is varying and avoid the curse of dimensionality in DNN.

Multi-hop communication with the aid of large-scale antenna arrays will play a vital role in future emergence communication systems. In this paper, we investigate amplify-and-forward based and multiple-input multiple-output assisted multi-hop communication, in which all nodes employ hybrid transceivers. Moreover, channel errors are taken into account in our hybrid transceiver design. Based on the matrix-monotonic optimization framework, the optimal structures of the robust hybrid transceivers are derived. By utilizing these optimal structures, the optimizations of analog transceivers and digital transceivers can be separated without loss of optimality. This fact greatly simplifies the joint optimization of analog and digital transceivers. Since the optimization of analog transceivers under unit-modulus constraints is nonconvex, a projection type algorithm is proposed for analog transceiver optimization to overcome this difficulty. Based on the derived analog transceivers, the optimal digital transceivers can then be derived using matrix-monotonic optimization. Numerical results obtained demonstrate the performance advantages of the proposed hybrid transceiver designs over other existing solutions.

This paper investigates the secrecy rate performance of a device-to-device (D2D) underlaid cellular network with an eavesdropper. Both the base station (BS) and the eavesdropper are assumed to have multiple antennas and apply minimum mean-square error (MMSE) receivers for detection. Different from the literature, mainly focused on enhancing the security of cellular communication using D2D jammers while neglected the secrecy performance of D2D communication, this paper aims to maximize the instantaneous weighted sum secrecy rate (IWSSR) of both cellular and D2D users. The maximization of IWSSR with respect to the transmit power of mobile users is a non-convex problem with non-differentiable objective function. In order to obtain efficient feasible solutions, we transform the IWSSR maximization problem to a min-max problem, relax the non-negative operator in secrecy rate expressions and then propose an alternative algorithm to solve the remaining problem. Simulation results show that the IWSSR of the network can be effectively increased by the proposed algorithm and, compared with exhaustive search (ES), the proposed algorithm performs very close to ES and involves much less computational complexity.

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.

With explosive increase of internet video services, perceptual modeling for video quality has attracted more attentions to provide high quality-of-experience (QoE) for end-users subject to bandwidth constraints, especially for compressed video quality. In this paper, a novel perceptual model for satisfied-user-ratio (SUR) on compressed video quality is proposed by exploiting compressed video bitrate changes and spatial-temporal statistical characteristics extracted from both uncompressed original video and reference video. In the proposed method, an efficient video feature set is explored and established to model SUR curves against bitrate variations by leveraging the Gaussian Processes Regression (GPR) framework. In particular, the proposed model is based on the recently released large-scale video quality dataset, VideoSet, and takes both spatial and temporal masking effects into consideration. To make it more practical, we further optimize the proposed method from three aspects including feature source simplification, computation complexity reduction and video codec adaption. Based on experimental results on VideoSet, the proposed method can accurately model SUR curves for various video contents and predict their required bitrates at given SUR values. Subjective experiments are conducted to further verify the generalization ability of the proposed SUR model.

Intelligent reflecting surfaces (IRSs) constitute a disruptive wireless communication technique capable of creating a controllable propagation environment. In this paper, we propose to invoke an IRS at the cell boundary of multiple cells to assist the downlink transmission to cell-edge users, whilst mitigating the inter-cell interference, which is a crucial issue in multicell communication systems. We aim for maximizing the weighted sum rate (WSR) of all users through jointly optimizing the active precoding matrices at the base stations (BSs) and the phase shifts at the IRS subject to each BS's power constraint and unit modulus constraint. Both the BSs and the users are equipped with multiple antennas, which enhances the spectral efficiency by exploiting the spatial multiplexing gain. Due to the non-convexity of the problem, we first reformulate it into an equivalent one, which is solved by using the block coordinate descent (BCD) algorithm, where the precoding matrices and phase shifts are alternately optimized. The optimal precoding matrices can be obtained in closed form, when fixing the phase shifts. A pair of efficient algorithms are proposed for solving the phase shift optimization problem, namely the Majorization-Minimization (MM) Algorithm and the Complex Circle Manifold (CCM) Method. Both algorithms are guaranteed to converge to at least locally optimal solutions. We also extend the proposed algorithms to the more general multiple-IRS and network MIMO scenarios. Finally, our simulation results confirm the advantages of introducing IRSs in enhancing the cell-edge user performance.

Channel estimation is conceived for hybrid multiple-input multiple-output (MIMO) communication systems. Both mean square error minimization and mutual information maximization are used as our performance metrics and a pair of low-complexity channel estimation schemes are proposed. In each scheme, the training sequence and the analog matrices of the transmitter and receiver are jointly optimized. We commence by designing the optimal training sequences and analog matrices for the first scheme. Upon relying on the resultant optimal structures, the training optimization problems are substantially simplified and the nonconvexity resulting from the analog matrices can be overcome. In the second scheme, the channel estimation and data transmission share the same analog matrices, which beneficially reduces the overhead of optimizing the associated analog matrices. Therefore, a composite channel matrix is estimated instead of the true channel matrix. By exploiting the statistical optimization framework advocated, the analog matrices can be designed independently of the training sequence. Based on the resultant analog matrices, the training sequence can then be efficiently designed according to diverse channel statistics and performance metrics. Finally, we conclude by quantifying the performance benefits of the proposed estimation schemes.

In this paper, the problem of maximizing sum-rate for uplink rate splitting multiple access (RSMA) communications is studied. In the considered model, each user transmits two messages to the base station (BS) with separate transmit power and the BS will use a successive decoding technique to decode the received messages. To maximize each user's transmission rate, the users must adjust their transmit power and the BS must determine the decoding order of the messages transmitted from the users to the BS. This problem is formulated as a sum-rate maximization problem with proportional rate constraints by adjusting the users' transmit power and the BS's decoding order. However, since the decoding order variable in the optimization problem is discrete, the original minimization problem with transmit power and decoding order variables can be transformed into a problem with only the rate splitting variable. Then, the optimal rate splitting of each user is determined. Given the optimal rate splitting of each user and a decoding order, the optimal transmit power of each user is determined. Next, the optimal decoding order is determined by an exhaustive search method. To further reduce the complexity of the optimization algorithm used for sum-rate maximization in RSMA, a user pairing based algorithm is introduced, which enables two users to use RSMA in each pair and also enables the users in different pairs to be allocated with orthogonal frequency. Simulation results show that RSMA can achieve up to 10.0%, 22.2%, and 83.7% gains in terms of rate compared to non-orthogonal multiple access (NOMA), frequency division multiple access (FDMA), and time division multiple access (TDMA).

Multi-resolution codebook based hierarchical beam training is an attractive solution to the heavy overhead of millimeter-wave (mmWave) beam alignment. However, most existing codebooks suffer from either undesired main-lobefluctuation or high hardware-complexity. To address these issues, this paper proposes a closed-form phase-shifted discrete Fourier transformation (PS-DFT/CF) codebook design for mmWave links with hybrid structures. The proposed codebook is of hybrid analog/digital architecture. Analog components are fixed-size DFT vectors, which can be readily implemented with radiofrequency (RF) phase shifters. Digital components at baseband select unitary subbeams shaped by the analog components and tackle the impact of the phase difference between adjacent subbeams, such that multi-resolution flat beam patterns can be synthesized. Moreover, the closed-form PS-DFT codebook design enables joint transceiver design for mmWave beam alignment. Numerical results verify that the proposed PS-DFT/CF codebook approaches the performance of the ideal PS-DFT counterpart.

Unmanned aerial vehicles (UAVs) for wireless communications have rapidly grown into a research hotspot as the mass production of high-performance, low-cost, and intelligent UAVs becomes practical. In the meantime, the fifth generation (5G) wireless communication and Internet-of-Things (IoT) technologies are being standardized and planned for global deployment. During this process, UAVs are becoming an important part of 5G and IoT, and expected to play a crucial role in enabling more functional diversity for wireless communications. In this paper, we first present a summary of mainstream UAVs and their use in wireless communications. Then, we propose a hierarchical architecture of UAVs with multilayer and distributed features to facilitate the integration of different UAVs into the next-generation wireless communication networks. Finally, we unveil the design tradeoffs with the consideration of power transfer, wireless communication, and aerodynamic principles. In particular, empirical models and published measurement data are used to analyze power transfer efficiency, and meteorological impacts on UAVs enabled next-generation wireless communications.

Small-cell aided ultra-dense networks (UDNs) constitute an efficient solution to the ever-increasing thirst for more data. Thanks to the vast untapped high-frequency spectrum of visible light, visible light communications (VLCs) are a natural candidate for UDN. In this paper, layered asymmetrically clipped optical OFDM (LACO-OFDM) aided ultra-dense VLC (UD-VLC) is investigated in terms of its user association, multiple access (MA), and resource allocation. To handle the severe inter-cell interference (ICI) amongst the densely deployed access points, we propose a novel overlapped clustering technique relying on a hybrid non-orthogonal MA and orthogonal MA scheme for enhancing the performance, with the aid of our dynamic resource allocation conceived. Our simulations show that the proposed LACO-OFDM aided UD-VLC using our hybrid MA scheme is more robust against the ICI, at a price of modestly decreasing the sum throughput.

In this paper, we investigate the security of a cache-aided multi-relay communication network in the presence of multiple eavesdroppers, where each relay can pre-store a part of the requested files in order to assist secure data transmission from source to destination. If the relays have cached the requested file, then they can directly send it to the destination; otherwise, traditional dual-hop data transmission is used. For both cases, relay selection is performed to assist the secure data transmission. We analyze the network secrecy performance in both scenarios ofnon-colludingandcolludingeavesdroppers, and obtain a closed-form expression for the average secrecy outage probability (SOP), as well as an asymptotic expression for the high main-to-eavesdropper ratio (MER). Through minimizing the network SOP, we further optimize the cache placement by proposing a stochastic sampling based cache learning (SacLe) strategy, which can be implemented in parallel and thus reduces the implementation latency substantially. Numerical and simulation results are finally presented to verify the proposed analysis, and show that the caching strategy has a significant impact on the network secrecy performance through affecting the caching diversity gain and signal cooperation gain at the relays. The proposed SacLe strategy is shown to be able to achieve the optimal performance obtained by the brute force (BF) algorithm.

In this paper, we investigate a multi-cell millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) network with low-precision analog-to-digital converters (ADCs) at the base station. Each cell serves multiple users and each user is equipped with multiple antennas but driven by a single RF chain. We first introduce a channel estimation strategy for the mmWave massive MIMO network and analyze the achievable rate with imperfect channel state information. Then, we derive an insightful lower bound for the achievable rate, which becomes tight with a growing number of users. The bound clearly demonstrates the impacts of the number of antennas and the ADC precision, especially for a single-cell mmWave network at low signal-to-noise ratio. It characterizes the tradeoff among various system parameters. Our analytical results are finally confirmed by extensive computer simulations.

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.

This paper investigates the physical layer security issue of a device-to-device (D2D) underlaid cellular system with a multi-antenna base station (BS) and a multi-antenna eavesdropper. To investigate the potential of D2D communication in improving network security, the conventional network without D2D users (DUs) is first considered. It is shown that the problem of maximizing the sum secrecy rate (SR) of cellular users (CUs) for this special case can be transformed to an assignment problem and optimally solved. Then, a D2D underlaid network is considered. Since the joint optimization of resource block (RB) allocation, CU-DU matching and power control is a mixed integer programming, the problem is difficult to handle. Hence, the RB assignment process is first conducted by ignoring D2D communication, and an iterative algorithm is then proposed to solve the remaining problem. Simulation results show that the sum SR of CUs can be greatly increased by D2D communication, and compared with the existing schemes, a better secrecy performance can be obtained by the proposed algorithms.

The tradeoff between narrow beams and low training overhead is deemed to a bottleneck for millimeter-wave (mmWave) systems. Particularly, in multi-user (MU) scenarios, the training overhead grows linearly with the number of users. To address this issue, this paper proposes a codebook based simultaneous beam training scheme for mmWave MU systems with split/partially-connected structures. In this scheme, the multi-resolution codebook based hierarchical beam searching is adopted to reduce the training overhead and acquire the angle of departure (AoD) for each user-group. Meanwhile, the concurrent beams, offered by the corresponding subarray-groups of split structures, enable simultaneous beam training for all served user-groups. Consequently, the overall training overhead is significantly reduced. Moreover, a two-phase training protocol is elegantly built, where the whole array is used to reap large array gain in the initial phase. Furthermore, in the subsequent parallel training phase, multiple subarray-groups conduct simultaneous beam training for the corresponding user-groups. Hence, advantages on both low training overhead and large array gain are achieved. Numerical results show the proposed scheme outperforms traditional counterparts, especially in moderate-high signal-to-noise ratio (SNR) regimes with limited transmission blocks.

Hierarchical beam training methods are imposed with some limitations on antenna spacing, the number of antennas and/or radio-frequency (RF) chains, which can not be directly extended to general deployments. To address these issues, this paper investigates a coordinated subarray based beam training scheme for indoor sub-Terahertz (sub-THz) multi-user (MU) communications with split hybrid structures. The capability of concurrent beams offered by multiple subarrays in the hybrid structure is exploited to partition the whole angle domain into multiple orthogonal subregions on the basis of subarrays, and each subregion is exclusively covered with a corresponding subarray without explicit interference among them, such that these multiple narrow beams can be simultaneously shaped in their reduced subregions. Consequently, an increased success rate of angle of departure (AoD) estimation can be obtained with affordable overhead in typical deployments, without limitation on the number of antennas in each subarray. Analysis and simulation results verify the feasibility and the superiority of the proposed scheme.

This paper studies energy efficient resource allocation for a machine-to-machine enabled cellular network with nonlinear energy harvesting, especially focusing on two different multiple access strategies, namely nonorthogonal multiple access (NOMA) and time division multiple access (TDMA). Our goal is to minimize the total energy consumption of the network via joint power control and time allocation while taking into account circuit power consumption. For both NOMA and TDMA strategies, we show that it is optimal for each machine type communication device (MTCD) to transmit with the minimum throughput, and the energy consumption of each MTCD is a convex function with respect to the allocated transmission time. Based on the derived optimal conditions for the transmission power of MTCDs, we transform the original optimization problem for NOMA to an equivalent problem which can be solved suboptimally via an iterative power control and time allocation algorithm. Through an appropriate variable transformation, we also transform the original optimization problem for TDMA to an equivalent tractable problem, which can be iteratively solved. Numerical results verify the theoretical findings and demonstrate that NOMA consumes less total energy than TDMA at low circuit power regime of MTCDs, while at high circuit power regime of MTCDs TDMA achieves better network energy efficiency than NOMA.

This letter investigates joint power control and user clustering for downlink nonorthogonal multiple access systems. Our aim is to minimize the total power consumption by taking into account not only the conventional transmission power but also the decoding power of the users. To solve this optimization problem, it is firstly transformed into an equivalent problem with tractable constraints. Then, an efficient algorithm is proposed to tackle the equivalent problem by using the techniques of reweighted ℓ1-norm minimization and majorization-minimization. Numerical results validate the superiority of the proposed algorithm over the conventional algorithms including the popular matching-based algorithm.

We investigate a general channel estimation problem in the massive multiple-input multiple-output system which employs the hybrid analog/digital precoding structure with limited radio-frequency (RF) chains. By properly designing RF combiners and performing multiple trainings, the proposed channel estimation can approach the performance of fully-digital estimations depending on the degree of channel spatial correlation and the number of RF chains. Dealing with the hybrid channel estimation, the optimal combiner is theoretically derived by relaxing the constant-magnitude constraint in a specific single-training scenario, which is then extended to the design of combiners for multiple trainings by sequential and alternating methods. Further, we develop a technique to generate the phase-only RF combiners based on the corresponding unconstrained ones to satisfy the constant-magnitude constraints. The performance of the proposed hybrid channel estimation scheme is examined by simulations under both nonparametric and spatial channel models. The simulation results demonstrate that the estimated channel state information can approach the performance of fully-digital estimations in terms of both mean square error and spectral efficiency. Moreover, a practical spatial channel covariance estimation method is proposed and its effectiveness in hybrid channel estimation is verified by simulations.

In this paper, we aim to minimize the average file transmission delay via bandwidth allocation and cache placement in two-tier heterogeneous networks with limited storage capacity, which consists of cache capacity and buffer capacity. For average delay minimization problem with fixed bandwidth allocation, although this problem is nonconvex, the optimal solution is obtained in closed form by comparing all locally optimal solutions calculated from solving the Karush-Kuhn-Tucker conditions. To jointly optimize bandwidth allocation and cache placement, the optimal bandwidth allocation is first derived and then substituted into the original problem. The structure of the optimal caching strategy is presented, which shows that it is optimal to cache the files with high popularity instead of the files with big size. Based on this optimal structure, we propose an iterative algorithm with low complexity to obtain a suboptimal solution, where the closed-from expression is obtained in each step. Numerical results show the superiority of our solution compared with the conventional cache strategy without considering cache and buffer tradeoff in terms of delay.

In this paper, we consider the sum α-fair utility maximization problem for joint downlink (DL) and uplink (UL) transmissions of a wireless powered communication network via time and power allocation. In the DL, the users with energy harvesting receiver architecture decode information and harvest energy based on simultaneous wireless information and power transfer. While in the UL, the users utilize the harvested energy for information transmission, and harvest energy when other users transmit UL information. We show that the general sum α-fair utility maximization problem can be transformed into an equivalent convex one. Trade-offs between sum rate and user fairness can be balanced via adjusting the value of α. In particular, for zero fairness, i.e., α = 0, the optimal allocated time for both DL and UL is proportional to the overall available transmission power. Trade-offs between sum rate and user fairness are presented through simulations.

In this paper, we study the hybrid precoding structures over limited feedback channels for massive multiuser multiple-input multiple-output (MIMO) systems. We focus on the system performance of hybrid precoding under a more realistic hardware network model, particularly, with inevitable dissipation. The effect of quantized analog and digital precoding is characterized. We investigate the spectral efficiencies of two typical hybrid precoding structures, i.e., the sub-connected structure and the fully connected structure. It is revealed that increasing signal power can compensate for the performance loss incurred by quantized analog precoding. In addition, by capturing the nature of the effective channels for hybrid processing, we employ a channel correlation-based codebook and demonstrate that the codebook shows a great advantage over the conventional random vector quantization codebook. It is also discovered that, if the channel correlation-based codebook is utilized, the sub-connected structure always outperforms the fully connected structure in either massive MIMO or low signal-to-noise ratio scenarios; otherwise, the fully-connected structrue achieves better performance. Simulation results under both Rayleigh fading channels and millimeter wave (mm-wave) channels verify the conclusions above.

In this paper, we investigate the problems of sum power minimization and sum rate maximization for multi-cell networks with non-orthogonal multiple access. Considering the sum power minimization, we obtain closed-form solutions to the optimal power allocation strategy and then successfully transform the original problem to a linear one with a much smaller size, which can be optimally solved by using the standard interference function. To solve the nonconvex sum rate maximization problem, we first prove that the power allocation problem for a single cell is a convex problem. By analyzing the Karush-Kuhn-Tucker conditions, the optimal power allocation for users in a single cell is derived in closed form. Based on the optimal solution in each cell, a distributed algorithm is accordingly proposed to acquire efficient solutions. Numerical results verify our theoretical findings showing the superiority of our solutions compared with the orthogonal frequency division multiple access and broadcast channel.

In this paper, we consider the network utility maximization problem with various user priorities via jointly optimizing user association, load distribution, and power control in a load-coupled heterogeneous network. In order to tackle the nonconvexity of the problem, we first analyze the problem by obtaining the optimal resource allocation strategy in closed form and characterizing the optimal base station load distribution pattern. Both observations are shown essential in simplifying the original problem and making it possible to transform the nonconvex load distribution and power control problem into convex reformulation via exponential variable transformation. An iterative algorithm with low complexity is accordingly presented to obtain a suboptimal solution to the joint optimization problem. Simulation results show that the proposed algorithm achieves better performance than conventional approaches.

In this paper, we study the hybrid precoding structures for massive multiuser multiple-input multiple-output (MIMO) systems. Particularly, the practical hardware network models with insertion loss are developed. The achievable rates of two typical hybrid precoding structures, the fully-connected structure and the sub- connected structure, are investigated, from which we discover that the sub-connected structure always outperforms the fully-connected structure in terms of the achievable rate in massive MIMO. We further characterize the effect of quantized analog precoding which indicates that the subconnected structure is able to achieve better performance with fewer feedback bits than the fully-connected structure. We also propose a channel statistics-based codebook used for the digital precoding stage which is more suitable for hybrid precoding systems than the conventional random vector quantization (RVQ) codebook.

The widely deployed cellular network, assisted with device-to-device (D2D) communications, can provide a promising solution to support efficient and reliable vehicular communications. In this paper, we identify differentiated requirements for different types of vehicular links, i.e., high capacity for vehicle-to-infrastructure (V2I) links and ultra reliability for vehicle-to-vehicle (V2V) links, and attempt to maximize the ergodic capacity of V2I connections while ensuring reliability guarantee for each V2V link. To account for fast channel variations caused by high mobility, we propose to perform spectrum sharing and power allocation based only on slowly varying large-scale fading information of wireless channels. A novel algorithm that yields optimal resource allocation and is robust to channel variations is proposed. Their desirable performance is confirmed by computer simulation.

Indoor positioning for visible light communication (VLC) has gained significant attentions recently with the popularity of light-emitting diodes (LEDs). In this paper, we consider a typical application of VLC by proposing a three-dimensional positioning scheme for a target terminal equipped with multiple photodiodes (PDs). Given the relative coordinates between the target terminal and receiving PDs along with positions of fixed transmitting LEDs, precise location estimation of the terminal device can be achieved via measuring received signal strength (RSS) through line-of-sight (LoS) channels. Moreover, multipath reflections from interior walls are considered as a major interference in non-LoS environment. It is discovered that the positioning error increases linearly with respect to the reflection coefficient of walls, which also verified by simulation results. The positioning error is achieved in millimeter scale under an ideal condition and in decimeter scale with multipath reflections.

One-bit digital-to-analog converter (DAC) has been a promising potential for both cost- and power-efficient massive multiple-input multiple-output (MIMO) implementation. We investigate the performance of a downlink massive MIMO with the 1-bit DAC using regularized zero-forcing (RZF) precoding serving quantized receivers. By taking the quantization errors at both transmitter and receivers, regularization parameter for the RZF is optimized with closed-form solution by applying asymptotic random matrix theory. The optimal parameter is discovered as linearly increasing w.r.t. the user loading ratio. Furthermore, asymptotic sum rate performance is derived and a closed-form expression of the optimal user loading ratio is achieved specifically for low SNR. The optimal user loading is found decreasing with increasing receiver quantization resolutions. Numerical simulations verify our observations.

We consider the problem of minimizing the total transmission power for a OFDM network where mutual interference exists among cells, with the power and load constraints for each base station (BS) and the rate demand constraint for every user. To solve the power minimization problem, we develop a distributed resource allocation and power control algorithm with low complexity. The complexity of the proposed algorithm is also analyzed. Numerical results show that the proposed algorithm is superior to the conventional schemes in terms of power consumption.

In this paper, we investigate the sum energy minimization problem in an uplink machine-to- machine (M2M) system with energy harvesting. To solve this nonconvex sum energy minimization problem, we first transform it into an equivalent convex problem and provide the optimal condition of the original problem. Then, we propose a low- complexity iterative scheme, which yields the optimal solution. The complexity of the proposed scheme is also analyzed. Numerical results show that the proposed scheme can achieve good performance.

In a visible light communication (VLC) system, the nonlinearity of LED is one of the challenges that prevents reliable communication. In order to mitigate the nonlinearity of LED, in this paper, the concept of spatial summing pulse amplitude modulation (PAM) is developed where PAM signals are separated into several on#x002F;off keying (OOK) signals transmitted by different LED groups. The signal streams from different LED groups are designed to sum in space into a single signal stream which is detected by a conventional optimal maximum likelihood detector. A scheme is proposed to cope with the distortion caused by the mismatch among the LED array in the spatial summing PAM system and the error performance of the spatial summing PAM is analyzed. Simulation results verify that the proposed scheme can remarkably improve the error performance.

In this letter, a multiuser relay network with massive multiple-input multiple-output is investigated with mixed-analog-to-digital converter (ADC) at receiver. We first characterize the uplink achievable rate by deriving a tight approximation, which embraces the conventional unquantized system as a special case. Both power scaling laws at sources and relay are presented. It is validated that the performance loss due to low-precision ADCs can be compensated by increasing the number of antennas M, obeying a logarithmical scaling law, rather than by increasing the transmit power at sources and/or relay. We show that the performance suffers from a loss factor interpreted as a nominal effective resolution of the entire mixed-ADC structure. Simulation results verify our observations.

The widely deployed cellular network, assisted with device-to-device (D2D) communications, can provide a promising solution to support efficient and reliable vehicular communications. Fast channel variations caused by high mobility in a vehicular environment need to be properly accounted for when designing resource allocation schemes for the D2D-enabled vehicular networks. In this paper, we perform spectrum sharing and power allocation based only on slowly varying large-scale fading information of wireless channels. Pursuant to differing requirements for different types of links, i.e., high capacity for vehicle-to-infrastructure (V2I) links and ultrareliability for vehicle-to-vehicle (V2V) links, we attempt to maximize the ergodic capacity of the V2I connections while ensuring reliability guarantee for each V2V link. Sum ergodic capacity of all V2I links is first taken as the optimization objective to maximize the overall V2I link throughput. Minimum ergodic capacity maximization is then considered to provide a more uniform capacity performance across all V2I links. Novel algorithms that yield optimal resource allocation and are robust to channel variations are proposed. Their desirable performance is confirmed by computer simulation.

In the above paper[1], the text discussion of several equations were misrepresented. Below is the corrected text ofSections IIIandIV, in which the errors appear.

We consider a multi-pair massive multiple-input multiple-output relay network, where the relay is equipped with a large number, N, of antennas, but driven by a far smaller number, L, of radio-frequency (RF) chains. We assume that K pairs of users are scheduled for simultaneous transmission, where K satisfies 2K=L. A hybrid signal processing scheme is presented for both uplink and downlink transmissions of the network. Analytical expressions of both spectral efficiency (SE) and energy efficiency (EE) are derived with respect to the RF chain number under imperfect channel estimation. It is revealed that, under the condition N>⌊4L2/π⌋, the transmit power of each user and the relay can be, respectively, scaled down by 1/√N and 2K/√N if pilot power scales with signal power, or they can be, respectively, scaled down by 1/N and 2K/N if the pilot power is kept fixed, while maintaining an asymptotically unchanged SE. While regarding EE of the network, the optimal EE is shown to be achieved when Pr= 2K Ps, where Prand Ps, respectively, refer to the transmit power of the relay and each source terminal. We show that the network EE is a quasi-concave function with respect to the number of RF-chains which, therefore, admits a unique globally optimal choice of the RF-chain number. Numerical simulations are conducted to verify our observations.

Ultra-dense cloud radio access networks (C-RANs) are an example of the architectures that will be critical components of the next-generation wireless systems. In a C-RAN architecture, an amorphous cellular framework, where each user connects to a few nearby remote radio heads (RRHs) to form its own cell, appears to be promising. In this paper, we study the ergodic capacity of such amorphous cellular networks at high signal-to-noise ratios (SNRs) where we model the distribution of the RRHs by a Poisson point process. We derive tractable approximations of the ergodic capacity at high-SNRs for arbitrary antenna configurations, and tight lower bounds for the ergodic capacity when the numbers of antennas are the same at both ends of the link. In contrast to prior works on distributed antenna systems, our results are derived based on random matrix theory and involve only standard functions which can be much more easier evaluated. The impact of the system parameters on the ergodic capacity is investigated. By leveraging our analytical results, we propose two efficient scheduling algorithms for RRH selection for energy-efficient transmission. Our algorithms offer a substantial improvement in energy efficiency compared with the strategy of connecting a fixed number of RRHs to each user.

We propose a pilot-data transmission overlay scheme for multipair massive multiple-input multiple-output (MIMO) relaying systems employing either half-duplex or full-duplex (FD) communications at the relay station (RS). In the proposed scheme, pilots are transmitted in partial overlap with data to decrease the channel estimation overhead. The RS can detect the source data with minimal destination pilot interference by exploiting the asymptotic orthogonality of massive MIMO channels. Then pilot-data interference can be effectively suppressed with the assistance of the detected source data in the destination channel estimation. Due to the transmission overlay, the effective data period is extended, hence improving system throughput. Both theoretical and simulation results confirm that the proposed pilot-data overlay scheme outperforms the conventional separate pilot-data design in the limited coherence interval scenario. Moreover, at asymptotically high and low SNR regions, the proposed scheme is superior regardless of the coherence interval length. Because of simultaneous transmission, the proper power allocation of source data transmission and relay data forwarding can further improve the system performance. Hence a power allocation problem is formulated and a successive convex approximation approach is proposed to solve the non-convex optimization problem with the FD pilot-data transmission overlay.

White light-emitting diode (LED) consisting of red, green, blue, and amber chips (RGBA-LED) has recently been adopted as transmitter in visible light communication (VLC) systems. This paper proposed a reshaped orthogonal frequency division multiplexing (R- OFDM) scheme for RGBA-LED-based VLC systems. In the R- OFDM, the signal is adjusted by separation and biasing after clipping (BAC) operations for transmitting in RGBA-LED. Then, the biasing factor of BAC is derived according to the color mix ratio (CMR) of RGBA-LED. At the receiver, We develop a direct detection algorithm to recover the original data, and analyze its the theoretical bit error rate (BER). Furthermore, a lower bound of the BER is analyzed under high clipping ratio (CR) by using SNR upper bound. Finally, simulation results confirm our theoretical analysis and verify that R-OFDM outperforms the ACO-OFDM in term of BER.

This paper considers the optimal energy efficient association for HetNets by applying almost blank subframes (ABSs) techniques. Aiming at maximizing energy efficiency (EE) without the quality of service (QoS) constraints, we obtain a closed-form optimal solution, which indicates that the optimal choice of blank resource block (RB) fraction for EE maximization is binary without individual user QoS requirements. We further incorporate QoS constraints and equivalently transform the corresponding complicated fractional EE optimization problem to a single linear program (LP) by introducing some new auxiliary variables. Finally, we confirm the validity of the assumption that a user is served by at most one BS in a given RB both in theory and by numerical results.

We consider a multi-pair massive multiple-input multiple-output (MIMO) two-way relay network, where multiple pairs of single-antenna users exchange data with the help of a multi-antenna relay station. We assume that the relay is equipped with a large antenna array, but driven by a far smaller number of RF chains, which can enormously reduce the hardware cost and complexity for practical applications. We propose a hybrid precoding scheme for both uplink and downlink of the network and analyze the sum rate performance under imperfect channel estimate. Numerical results verify that the proposed scheme incurs limited degradation compared to traditional full-dimensional digital precoding. Given the relationship between the number of relay antennas, N, and the RF chain number, L, satisfying N > 4L2/π, we further discover that the transmit power of each user and the relay can be respectively scaled down by a factor of 1 /√N and L/√N if the pilot power scales with the signal power, or by a factor of 1/N and L/N if the pilot power is kept fixed, while maintaining an asymptotically unchanged performance.

In this paper, we consider the problem of maximizing the weighted sum energy efficiency (WS-EE) for multi-input single-output (MISO) interference channels (ICs), which are well acknowledged as general models of heterogeneous networks (HetNets), multicell networks, etc. To address this problem, we develop an efficient distributed beamforming algorithm based on a pricing mechanism. Specifically, we carefully introduce a price metric for distributed beamforming design, which fortunately allows efficient closed-form solutions to the per-user beam-vector optimization problem. The convergence of the distributed pricing-based beamforming design is theoretically proven. Furthermore, we present an implementation strategy of the proposed distributed algorithm with limited information exchange. Numerical results show that our algorithm converges much faster than existing algorithms, while yielding comparable, sometimes even better performance in terms of the WS-EE. Finally, by taking the backhaul power consumption into account, it is interesting to show that the proposed algorithm with limited information exchange achieves better WS-EE than the full information exchange-based algorithm in some special cases.

We study transceiver optimization in the context of amplify-and-forward (AF) multiple-input multiple-output (MIMO) relaying with source-destination link. In particular, by applying the known results of the optimal destination receiver and relay precoder, we mainly focus on the source beamformer optimization which manifests itself as a difficult non-convex problem. To find a globally optimal solution to this problem, we first transform it into an equivalent solvable convex problem via semidefinite relaxation (SDR). Inspired by the SDR based approach, we conduct a further analysis and successfully derive a novel semi-closed form solution to the optimal source beamformer. It is interesting to observe that the solution possesses a generalized eigenmode transmission structure depending on a weighted sum of the Gram matrices of both source-relay and source-destination channels.

This paper presents an analytical performance investigation of both beamforming (BF) and interference cancellation (IC) strategies for a device-to-device (D2D) communication system underlaying a cellular network with an M-antenna base station (BS). We first derive new closed-form expressions for the ergodic achievable rate for BF and IC precoding strategies with quantized channel state information (CSI), as well as, perfect CSI. Then, novel lower and upper bounds are derived which apply for an arbitrary number of antennas and are shown to be sufficiently tight to the Monte-Carlo results. Based on these results, we examine in detail three important special cases including: high signal-to-noise ratio (SNR), weak interference between cellular link and D2D link, and BS equipped with a large number of antennas. We also derive asymptotic expressions for the ergodic achievable rate for these scenarios. Based on these results, we obtain valuable insights into the impact of the system parameters, such as the number of antennas, SNR and the interference for each link. In particular, we show that an irreducible saturation point exists in the high SNR regime, while the ergodic rate under IC strategy is verified to be always better than that under BF strategy. We also reveal that the ergodic achievable rate under perfect CSI scales as log2M, whilst it reaches a ceiling with quantized CSI.

In this paper, we investigate a multiuser MIMO downlink with imperfect channel state information (CSI) from the physical-layer security provision. In a classical transmitter (Alice)-legitimate receiver (Bob)-eavesdropper (Eve) model using artificial noise to disturb Eve's reception, we consider a general scenario where all nodes are equipped with multiple antennas, and Bob's CSI is acquired by pilot-assisted channel estimation. For designing the secure transmit beamforming, we utilize random matrix quantization (RMQ) to quantize Bob's channel estimate, and then Bob feeds it back to Alice. Due to the effects of imperfect CSI at Alice, the secrecy performance is upper bounded in the high signal-to-noise ratio (SNR) regions. In order to avoid the interference-limited phenomenon, we present a scaled strategy for the secrecy system utilizing derived upper bound on the secrecy rate loss. Moreover, it is shown that our derived results can be easily extended to a special case where both legitimate and eavesdropping receivers equip a single antenna each, where random vector quantization (RVQ) is utilized instead. By employing our proposed feedback strategy, the secrecy rate increases with transmit power, and the certain secrecy requirement can be guaranteed.

In this paper, we investigate robust beamforming to improve the energy efficiency (EE) of wireless networks when only imperfect or partial channel state information (CSI) is available at the transmitter. Due to CSI quantization errors and/or limited feedback information, CSI imperfections can be well modeled by a bounded uncertainty region. We focus on the worst case robust beamforming strategy to optimize the EE of downlink transmission under the deterministic bounded channel model, which merely assumes a maximal channel error magnitude. We start with a single-user single-cell MIMO system and obtain a closed-form design for robust beamforming. For a multicell network, robust beamforming is in a nonconvex fractional form, and the solution cannot be directly extended from the single-cell scenario. To solve this problem efficiently, we resort to a lower bound, instead of the primal problem, and cast it as a semidefinite program (SDP). The robustness and efficiency of the proposed beamforming design are confirmed by computer simulation results.

In this letter, we study physical-layer secure transmission for a multi-antenna relay system in the presence of multiple eavesdroppers, with emphasis on quality-of-service (QoS) based optimization. In particular, our ultimate goal is to minimize relay transmit power and meanwhile guarantee the fulfillment of QoS constraints with regard to both legitimate receiver and eavesdroppers. Instead of directly dealing with this intricate problem, we first prove that the optimal relay precoder possesses a generalized channel matching structure, which allows us to transform the original problem into a tractable semi-definite programming (SDP). We then rigorously verify the optimality of this approach, and further discuss its extension to the scenario with only channel statistics. Simulations are finally performed to demonstrate the superiority of the proposed optimal design.

This letter investigates secrecy performance of a wiretap channel where an eavesdropper is located in close proximity to legitimate receiver. In the system, both receivers equip multiple antennas and use maximal ratio combining (MRC) to harvest diversity gains. In order to analyze the impact of eavesdropping in close proximity, we derive closed-form expressions for the system performance in terms of both secrecy capacity and outage probability. Since the derived expressions involve infinite series sums, finite series approximations are then presented with guaranteed truncation error. Moreover, we also characterize the asymptotic behavior of secrecy outage. Some insightful observations are accordingly manifested with numerical verifications.

In this paper, we consider the problem of maximizing the energy efficiency (EE) for multiple-input-multiple-output (MIMO) interference channels (ICs), subject to the per-link power constraint. To avoid extensive information exchange among all links, the optimization problem is formulated as a noncooperative game, where each link maximizes its own EE. We show that this game always admits a Nash equilibrium (NE) and the sufficient condition for the uniqueness of the NE is derived for the case of large enough maximum transmit power constraint. To reach the NE of this game, we develop a totally distributed EE algorithm, in which each link updates its own transmit covariance matrix in a completely distributed and asynchronous way. Some players may update their solutions more frequently than others or even use the outdated interference information. The sufficient conditions that guarantee the global convergence of the proposed algorithm to the NE of the game have been given as well. We also study the impact of the circuit power consumption on the sum EE performance of the proposed algorithm in the case when the links are separated sufficiently far away. Moreover, the tradeoff between the sum EE and the sum spectral efficiency (SE) is investigated with the proposed algorithm under two special cases: 1) low transmit power constraint regime; and 2) high transmit power constraint regime. Finally, extensive simulations are conducted to evaluate the impact of various system parameters on the system performance.

With the rapidly increasing demand for high speed data transmission and a growing number of terminals in one cell, massive multiple-input multiple-output (MIMO) has been shown promising owing to its high spectrum efficiency. Although massive MIMO can efficiently improve the system performance, usage of orthogonal pilots and growing terminals cause large resource consumption especially when the coherence interval is short. This paper presents a semi-orthogonal pilot design with simultaneous data and pilot transmission. In the proposed technique, we exploit the asymptotic channel orthogonality in massive MIMO systems, with which the mutual interference between data and pilot can be mitigated by successive interference cancellation (SIC). Theoretical analysis and simulation results show that the proposed pilot design can achieve a significant performance improvement with reduced pilot resource consumption compared with the popular orthogonal pilots.

In systems that allow device-to-device (D2D) communications, user pairs in close proximity communicate directly without using an access point (AP) as an intermediary. D2D communications leads to improved throughput, reduced power consumption and interference, and more flexible resource allocation. We show that the D2D paradigm also provides significantly improved security at the physical layer, by reducing exposure of the information to eavesdroppers from two relatively high-power transmissions to a single low-power hop. We derive the secrecy outage probability (SOP) for the D2D and cellular systems, and compare performance for D2D scenarios in the presence of a multi-antenna eavesdropper. The cellular approach is only seen to have an advantage in certain cases when the AP has a large number of antennas and perfect channel state information.

DFT-Spread OFDM has been well studied in radio frequency (RF) systems thanks to its effectiveness in reducing the peak-to-average power ratio (PAPR). However, due to the inherent positive real signal constraint in visible light communications (VLC), the implementation of DFT-Spread OFDM in VLC faces some different challenges with respect to its high PAPR. This paper presents an LED array assisted visible light communication system using DFT-Spread OFDM. With this technique, theoretical analysis are firstly derived to help characterize the system PAPR reduction in VLC systems. Specifically, two different subcarrier allocation modes namely localized DFT-Spread and interleaved DFT-Spread OFDM are explicitly studied with their time domain signal expressions. Moreover, based on the results, detailed comparisons of DFT-Spread OFDM in terms of PAPR reduction are made for VLC and RF systems. Simulation results show that the proposed system not only leads to a reduced PAPR, but also achieves a performance gain in BER without any loss of system transmission rate.

This paper studies adaptive coordinated control of device-to-device (D2D) communications underlaying a cellular network with a multi-antenna base station (BS). We analyze the performance of cellular and D2D users under the channel imperfections including both quantization error and feedback delay. More specifically, we derive the received signal-to-interference-and-noise ratio (SINR) distributions of both D2D and cellular users. Based upon this, we further obtain a closed-form approximation of the achievable rate, which serves as a theoretical support for choosing transmit beamforming strategies adaptively.

Pilot contamination has become a main constraint for large-scale multi-input multi-output (MIMO) systems. In this paper, we consider a large-scale multi-cell MIMO where the number of users per cell K is proportional to that of base station antennas M. Both performances with and without pilot contamination are analyzed in a quantitative way with closed-form expressions. Unlike some existing works, we derive the result by coping with the fast fading explicitly, which makes our result far more accurate for moderate to large antenna numbers. From our derived results, it is found that the interference still exists when both M and K tend to infinity with a fixed ratio, and larger K/M results in a smaller performance degradation due to the pilot contamination in the multi-cell large-scale MIMO.

We study the robust transceiver design for amplify-and-forward (AF) multiple-input-multiple-output (MIMO) relay systems with a direct link and imperfect channel state information (CSI). Under this circumstance, we aim at obtaining the globally optimal transceiver that minimizes the mean-squared error (MSE) of symbol detection. Specifically, given a source beamformer, we first derive closed-form expressions for the optimal relay precoder and destination receiver. Then, to tackle the intricate non-convex problem with respect to source beamformer, we develop an efficient approach involving one-dimensional search and semidefinite programming (SDP). We prove rigourously that the proposed method yields a globally optimal solution. Simulation results verify the pronounced performance provided by the proposed design.

This paper investigates a multiuser MIMO relay downlink system with imperfect channel estimation and limited feedback. We analyze the achievable rate loss due to channel state information (CSI) mismatch arising from both channel estimation and quantization feedback. We first derive an upper bound to characterize the effect of imperfect CSI, and then present a limited feedback strategy for both the two-hop channels in the relay system. This newly presented feedback strategy reveals the relationship among the CSI quantization levels, the transmit power, and the pilots for channel estimation. An optimized pilot design at the base station and the relay is also presented by applying the derived bounds for the two-hop system.

Transceiver designs have been a key issue in guaranteeing the performance of multiple-input multiple-output (MIMO) relay systems, which are, however, often subject to imperfect channel state information (CSI). In this paper, we aim to design a robust MIMO transceiver for nonregenerative MIMO relay systems against imperfect CSI from a worst-case robust perspective. Specifically, we formulate the robust transceiver design, under the minimum mean-squared error (MMSE) criterion, as a minimax problem. Then, by decomposing the minimax problem into two subproblems with respect to the relay precoder and destination equalizer, respectively, we show that the optimal solution to each subproblem has a favorable channel-diagonalizing structure under some mild conditions. Based on this finding, we transform the two complex-matrix subproblems into their equivalent scalar forms, both of which are proven to be convex and can be efficiently solved by our proposed methods. We further propose an alternating algorithm to jointly optimize the precoder and equalizer that only requires scalar operations. Finally, the effectiveness of the proposed robust design is verified by simulation results.

In this paper, we investigate the performance enhanced transceiver design in a Device-to-Device (D2D) communication system underlaying a cellular network. The problem of joint transmitter and receiver optimization via maximizing the entire D2D and cellular transmission rate is considered. Due to the non-convexity of the primal problem, we resort to decomposing the problem into a sequence of standard convex quadratic programs. An iterative sequential optimization algorithm is accordingly presented for joint transceiver design at both the base station and the device terminals. Computer simulations convinced the performance enhancement of our proposed scheme compared with conventional transmission schemes.

The relay technology is effective in extending radio coverage and improving the performance of cell edge users. In multi-antenna relay channels, good knowledge of the channel state information (CSI) at the transmitter is important to achieve multiplexing gains of the multiple-input multiple-output technique. In this paper, we study the multi-antenna relay downlink channel with limited feedback CSI from both two-hop links. Data streams from the base station (BS) are first transmitted to a relay station (RS) with singular value decomposition-based precoding and receiver pulse shaping at the BS and RS, respectively. The block diagonalization precoding is then applied at the RS to forward the received signals to the remote multi-antenna users. We derive an upper bound for the system throughput loss due to CSI quantization error, and then propose a feedback quality control strategy to maintain a bounded rate loss relative to the perfect CSI case. It reveals that the feedback size B_1 from the RS to BS needs to scale in proportion to both transmit power at the BS and RS while the feedback size B_2 from each user to the RS only needs to scale linearly with the transmit power at the RS.

This paper considers device-to-device (D2D) communications underlaying cellular networks with a multi-antenna base station (BS). The BS serves its own cellular users while letting another remote terminal directly transmit signals to its nearby receiver via a D2D link. Two transmit strategies including beamforming (BF) and interference cancellation (IC) are considered at the BS for performance evaluation in terms of achievable channel capacity. The capacity performance of two different cases with perfect and quantized channel knowledge at the transmitter is derived with closed-form expressions. Based on these results, an adaptive transmission scheme to switch between BF and IC is proposed. Numerical results verify the accuracy of the derived expressions and draw the operating regions of BF/IC strategies.

This paper investigates a multiuser MIMO relay downlink system with imperfect channel estimation and limited feedback. Compared with the system using perfect channel state information (CSI), we analyze the achievable rate loss due to CSI mismatch arising from both channel estimation and CSI quantization feedback. An upper bound to the rate loss is firstly derived to characterize the effect of imperfect CSI. Given the rate loss bound, we then present a limited feedback strategy for both two-hop channels in the relay system to guarantee a bounded rate loss for growing SNRs. This newly presented strategy reveals the relationship between CSI quantization level and other system parameters, including the transmit power and the pilots for channel estimation, on the entire two-hop system. Finally, computer simulations are carried out for verifying the correctness of our derived results.

This paper studies the capacity performance of coordinated multi-point (CoMP) downlink transmissions based on limited feedback. We consider both path loss effects and channel imperfections including feedback delay and quantization error. Closed-form expressions are derived to characterize ergodic achievable rates for joint processing (JP) and coordinated beamforming (CBF) techniques, respectively. According to the derived expressions, an adaptive transmission strategy to switch between JP and CBF is proposed to maximize cell throughput. Simulation shows the CBF scheme is preferred at medium SNR with varying switch points jointly determined by the feedback size, delay, and locations of CoMP users.

In this paper, we develop a novel joint transmit and receive design for multiuser MIMO (MU-MIMO) systems. The proposed scheme exploits the channel temporal correlation and incorporates the matched filter (MF) combining vector into the beamforming design. Considering the beamforming is optimized with the outdated channel state information (CSI) feedback, we design the beamformer by maximizing the conditional expectation of signal-to-leakage-and-noise ratio (SLNR) given that a delayed version of CSI feedback is available. In this way, the optimized beamforming is able to alleviate the performance degradation due to the CSI delay. Numerical results show that our proposed scheme achieves almost the same performance as conventional iterative algorithms but with much lower complexity.

We study the performance of secure communications over correlated fading channels in the presence of an eavesdropper, where the main and eavesdropper channels are correlated. We derive exact expressions for both the average secrecy capacity and the outage probability in the form of infinite series. Moreover, the truncated error of the infinite series representations involved in the analytical results is also investigated. The accuracy of our performance analysis is verified by simulation results.

This paper investigates the beamforming design for multiuser downlink with limited feedback, where a practical channel state information (CSI) feedback model consisting both feedback delay and CSI quantization error is considered. Under this circumstance, we derive a closed-form multiuser beamforming scheme via minimizing the expected mean square error (MSE) with respect to the feedback imperfection. Compared with conventional MMSE beamforming scheme, we find that not only the regularization factor but the optimal MMSE beamforming structure changes due to the CSI imperfection, especially the feedback delay. Numerical results verify in different cases that the optimized beamforming design outperforms conventional ones in terms of both sum rate and BER performance.

Physical layer secret key can be extracted from wireless fading channels based on channel quantization with guardband (CQG). In this paper, we analyze the key generation efficiency of the CQG protocol. Analytical closed-form expressions of bit error rate (BER) and key generation efficiency are derived for the CQG protocol. In order to further improve the key generation efficiency, we concatenate the CQG protocol with reconciliation. And then, we present a upper bound of key generation efficiency, which can be maximized by careful selecting the guardband region and LDPC codes. The key generation efficiency is also analyzed by considering the attacks in the case of an unauthenticated wireless channel.

This paper studies the optimization problem for joint precoding design in a multi-antenna downlink channel using relaying. We formulate the joint source and relay precoding design by aiming at sum capacity maximization. Since this problem is in general nonconvex, we first convert this problem into standard convex quadratic programs, and then propose an iterative joint precoding optimization algorithm by utilizing efficient quadratic programming approaches. We observe that the iterative method always yields optimal precoding matrices which diagonalize the compound channel of the backward (source-to-relay) and the forward (relay-to-destination) links at high SNR regimes. Motivated by this observation, we further develop an efficient structured precoding design. Simulation results are presented to verify the effectiveness of our proposed precoding schemes.

Owing to its spectral efficiency, bidirectional relaying is a promising candidate for information exchange in multiple-user cooperative networks. When the network is limited by resource constraints, amplify-and-forward (AaF) relay protocol is often the choice due to its simplicity and ease of use. Power allocation for AaF protocol has being extensively studied in unidirectional relay networks but how it can be implemented in two-way multiple-relay multiple-user networks has yet to be addressed. In this paper, we consider the adaptive power allocation in bidirectional AaF multiple-relay multiple-user networks. We show that when the multiple-user interference can be removed by a robust channel assignment algorithm, power allocation by maximizing the instantaneous sum rate or minimizing the symbol error rate can be suitably casted as a geometric programming (GP) problem. Simulation results show adaptive power allocation by GP outperforms that of equal power allocation scheme particularly when there is a single serving relay, and the gain can be as substantial when there are multiple serving relays.

We consider a MIMO relay downlink system using precoding and limited feedback from two-hop channels. Since the conventional precoding matrices are directly obtained by treating the quantized channel state information (CSI) feedback as the real CSI, we propose an improved relay precoding strategy by taking the effect of channel quantization error into account. The proposed relaying technique is designed based on the minimum mean square error (MMSE) criterion and is robust to the channel uncertainties due to quantized CSI feedback. Numerical results verify the effectiveness of the proposed scheme.

This paper investigates the joint precoding optimization problem for a relay-assisted multi- antenna downlink system. Aiming at multiuser sum capacity maximization, we first propose an iterative optimization algorithm which exploits quadratic programming approaches. We observe that the iterative method always yields the optimized precoding matrices which diagonalize the compound channel of the system at high SNR regimes. Inspired by this observation, we further develop an efficient source and relay precoding strategy by diagonalizing the compound channel of the backward (source-to- relay) and the forward (relay-to-destination) links. Simulation results verify the effectiveness of our proposed precoding schemes.

We consider a MIMO relay downlink system using beamforming techniques with quantized channel state information (CSI) feedback from two hop channels. With this scheme, each remote user feeds back its quantized CSI to the relay, and the relay sends the quantized beamforming vectors to the base station (BS). An upper bound on the rate loss due to feedback quantization is first characterized. Then, a strategy of scaling the quantization accuracy of both hop links in order to maintain the rate loss within a predetermined gap for growing SNRs is proposed. It is revealed that the numbers of feedback bits of both links should scale linearly with the transmit power at the relay, while the number of feedback bits from the relay to the BS needs to grow with the transmit power at the BS.

In this paper, we show how to compute the Slepian-Wolf lower bound for the reconciliation of physical layer secret keys between two radio terminals. Two coding approaches, including the syndrome method and the parity method, have been proven to require the same number of bits exchanged between two terminals for the reconciliation in the noiseless environment. We also present a practical coding approach based on LDPC codes, and the gap between the practical approach and the Slepian-Wolf bound is given.

This paper investigates the user selection strategy in multiuser downlinks using zero-forcing beamforming (ZFBF) and finite-rate feedback (FRF). In order to mitigate the interference-limited effect, we propose an efficient user scheduling scheme combined with adaptive transmission mode selection to achieve a better diversity and multiplexing tradeoff in terms of the sum rate performance. In this scheme, each user first evaluates its preferred transmission mode and the corresponding achievable rate according to a derived tight lower bound to the rate. Given such information available through feedback, the base station then determines the global transmission mode of the system and selects users for simultaneous transmissions. Simulation results demonstrate the performance of our proposed scheme.

We investigate the problem of downlink user scheduling in multiple-input multiple-output (MIMO) systems withMtransmit antennas andKhomogeneous multiantenna receiving users. We develop a simple algorithm to schedule a subset ofMactive users for data transmission. We first identify a set ofLcandidate users that should provide channel information feedback. We then selectMactive users among theLcandidates based on joint consideration of their effective channel gains and directions. We derive an asymptotic upper bound for the sum rate gap between the maximum sum rate of theMselected active users, achieved by dirty paper coding, and the full sum capacity of the original MIMO broadcast channel. Utilizing the upper bound, we show that the sum rate gap can be reduced to meet user requirement by suitably choosing a large but still finiteL. Furthermore, we also investigate the performance of the low complexity zero-forcing beamforming (ZFBF) for theMactive users.

The authors study the problem of limited channel information feedback in multiple-input single-output (MISO) broadcast channels serving heterogeneous users. The heterogeneous users have different feedback channel capacities because of their physical constraints and limitations. Our objective is to design an optimised limited feedback strategy for multiple users by using a set of multi-resolution codebooks such that, under varying and quantised channel information feedback, the multiuser system can maximise its achievable sum rate. By exploiting the receive antenna combining technique, the authors further generalise the proposed scheme to the multiuser beamforming case with multi-antenna users. Finally, the authors verify the proposed scheme by numerical methods. Simulation results show that the sum rate performance for heterogeneous users can be effectively improved by using the proposed limited feedback scheme, which is optimised according to only statistic channel information of users.

This letter considers minimum mean square error beamforming (MMSEBF) for the downlink of a multiuser multiple-input single-output (MISO) system with limited feedback, where the transmitter only has quantized information regarding the direction of the channel. We present an improved approach which is robust to the channel uncertainties arising from the quantization errors and the lack of channel magnitude information. Simulation results show that the proposed robust MMSEBF scheme provides improved performance over the conventional beamforming schemes in limited feedback systems.

We investigate the design of a limited feedback mechanism for multiple-input-multiple-output (MIMO) broadcast systems that are equipped with automatic repeat request (ARQ) capabilities. Given multiple antennas at the base-station (BS) and single antenna subscribers, we focus on maximizing the ergodic system sum capacity and propose an adaptive feedback bit allocation scheme under the constraint of total feedback bandwidth. Furthermore, we develop two efficient feedback allocation schemes based on asymptotic analysis. We present simulation results that verify the effectiveness of the proposed adaptive allocation schemes.

This paper investigates the scheduling of user signals in MIMO systems with spatially correlated channels. By maximizing an upper bound of the sum capacity, we propose a scheduling scheme which requires only a single scalar feedback from each receiving user. To further reduce the need for user information feedback and better explore the spatial correlation information, a more efficient scheduling method is also developed. This new approach only requires a 1-bit indicator from each user and selects users according to the slowly varying MIMO channel correlation information. Numerical results verify the effectiveness of our proposed schemes.

This work investigates the adaptive resource allocation scheme for the downlink of multiuser OFDM systems. We focus on the problem of maximizing the overall spectral efficiency while maintaining the QoS requirements of users, including bit error rate and individual minimum rate requirements. Under the assumption of equal power allocation, an efficient algorithm is proposed to obtain the suboptimal solution of the resource allocation. In this algorithm, we first introduce some positive multipliers, one for each user, according to their minimum rate constraints (MRC), and then a parallel subcarrier-and-bit allocation scheme is designed using these multipliers with low complexity. The numerical results show that our algorithm not only substantially reduces the computational complexity of the existing algorithm, but also provides a noticeable performance improvement.

Orthogonal frequency division multiplexing (OFDM) is sensitive to carrier frequency offset (CFO), which destroys the orthogonality and causes inter-carrier interference (ICI). In this paper, we investigate the average error probability of OFDM systems impaired by CFO over frequency selective Rayleigh fading channels. By exploiting the Beaulieu series, the effects of CFO on the symbol error rate (SER) and bit error rate (BER) performance, which depend strongly on the statistical characteristics of the fading channels, are expressed as the sum of an infinite series in terms of the characteristic function (CHF) of the ICI. We only consider OFDM systems modulated with BPSK, QPSK, 8-PSK and 16-QAM, and the Gray-coded mapping are employed for the evaluation of BER performance.

We propose a computationally efficient semidefinite relaxation (SDR) of the maximum likelihood (ML) detector for 16 quadrature amplitude modulation (16-QAM) in multiple-input multiple-output (MIMO) systems. The SDR stems from a variant of the ML problem, which utilizes set operation algorithms to formulate the alphabet constraint. Theoretical analysis and numerical simulations demonstrate that the proposed method can provide improved error performance as compared to some previous detectors.