Mobile edge computing–space-air-ground integrated network (MEC-SAGIN) is emerging as a crucial component of future wireless systems. Despite its potential, addressing network fluctuations while ensuring continuous low-latency computing services in highly dynamic environments remains a significant challenge. To address this issue, this paper proposes a fluid antenna (FA)-assisted MEC-SAGIN system, which enhances channel transmission conditions and reduces uplink task offloading latency by flexibly adjusting the antenna ports of edge computing users equipped with FAs. Specifically, we aim to minimize the maximum total computational delay (TCD) of edge computing tasks for ground users (GUs) and the satellite user (SU) by jointly optimizing the task offloading strategies, computational resource allocation, FA port positions, unmanned aerial vehicle (UAV) location, and the receive beamforming matrix. To solve this non-convex problem, we employ the block coordinate descent (BCD) technique to decompose the original problem into four subproblems. The subproblems are optimized using a combination of low-complexity iterative algorithms and the projected gradient descent (PGD) method to refine communication and computation configurations as well as FA port selection. Simulation results demonstrate that the FA-assisted scheme significantly improves the TCD performance of the MEC-SAGIN system. It maintains transmission stability and reliability in dynamic environments while outperforming conventional fixed-position antennas (FPAs) and random-port antenna schemes.

With the advent of 6G communications, intelligent communication systems face multiple challenges, including constrained perception and response capabilities, limited scalability, and low adaptability in dynamic environments. To address these challenges, this tutorial provides a systematic and comprehensive introduction to the principles, design, and applications of Large Artificial Intelligence Models (LAMs) and Agentic AI technologies in intelligent communication systems, aiming to offer researchers an integrated overview of cutting-edge methodologies and practical insights. First, the tutorial outlines the background of 6G communications and reviews the technological evolution from LAMs to Agentic AI. It then systematically examines the key components required for constructing LAMs, classifies various types of LAMs, and analyzes their applicability in communication. A LAM-centric design paradigm tailored for communication systems is subsequently proposed, encompassing dataset construction, internal learning, and external learning approaches. Building upon this foundation, the tutorial develops an LAM-based Agentic AI system for intelligent communications, elaborating on its core components—including agents, world models, planners, knowledge bases, tools, and memory modules— as well as their interaction mechanisms. Finally, it provides an in-depth review of representative applications of LAMs and Agentic AI in communication scenarios, and summarizes the current research challenges and future directions, with the goal of fostering the development of efficient, secure, and sustainable next-generation intelligent communication systems.

In this paper, we propose a hybrid simultaneous wireless information and power transfer (SWIPT)–enabled multiple-input multiple-output (MIMO) architecture, where the base station (BS) uses a conventional radio-frequency (RF) transmitter for downlink transmission and a Rydberg atomic quantum receiver (RAQR) for receiving uplink signals from Internet of Things (IoT) devices. To fully exploit this integration, we jointly design the transmission scheme and the power-splitting strategy to maximize the weighted sum rate, which leads to a non-convex problem. To address this challenge, we first derive closed-form lower bounds on the uplink achievable rates for maximum ratio combining (MRC) and zero-forcing (ZF), as well as on the downlink rate and harvested energy for maximum ratio transmission (MRT) and ZF precoding. Building upon these bounds, we propose an iterative algorithm relying on the best monomial approximation and geometric programming (GP) to solve the non-convex problem. Finally, simulations validate the tightness of our derived lower bounds and demonstrate the superiority of the proposed algorithm over benchmark schemes. Importantly, by integrating RAQR with SWIPT-enabled MIMO, the BS can reliably detect weak uplink signals from IoT devices powered only by harvested energy, enabling battery-free IoT networks.

This paper investigates near-field extremely large-scale multiple-input multiple-output (XL-MIMO) systems with sparse uniform planar arrays (UPAs). Based on the Green’s function-based channel model, the paper derives closed-form expressions for the signal beam power when the distance coordinate or the angular coordinates varies with respective to the focused position. Based on that, closed-form expressions for the lobe length and the suppressing ratio are obtained, indicating that both the distance-focusing property and the grating lobe behavior can be enhanced as the focal distance decreases or the antenna spacing increases. Furthermore, the paper introduces a crucial constraint on system parameters, under which effective degrees-of-freedom (EDoF) of XL-MIMO systems with sparse UPAs can be precisely estimated. Then, the paper proposes an algorithm to obtain a closed-form expression, which can estimate EDoF with high accuracy and low computational complexity. The numerical results verifies the correctness of the main results of this paper.

This paper investigates a two-timescale uplink transmission framework for a fluid antenna-enabled multiuser multi-input multi-output system (MIMO-FAS). Antenna positions are optimized based on statistical channel state information (CSI), while beamforming vectors at the base station (BS) adapt to instantaneous CSI. Under a Rician fading channel with imperfect CSI, we establish a linear minimum mean square error (LMMSE)-based channel estimation approach and derive a closed-form expression for the achievable uplink rate using a low-complexity maximal-ratio-combining (MRC) detector. The optimization problem is formulated as a minimum user rate maximization problem by optimizing the fluid antenna positions, subject to the feasible region and the minimum spacing distance constraints. To address this non-convex problem, a genetic algorithm (GA) method is proposed, encoding antenna configurations as population individuals. Additionally, an accelerated gradient ascent algorithm is proposed to enhance computational efficiency. Numerical results validate the mathematical derivations and demonstrate that the proposed two-timescale transmission strategy significantly outperforms traditional FPA systems, with both algorithms achieving enhanced gains.

Integrating large-scale antenna arrays is essential for overcoming capacity limitations in wireless communications. In this work, we examine a novel sixth-generation (6G) secure simultaneous wireless information and power transfer (SWIPT) system, where a transmitter equipped with an extremely large-scale antenna array (ELAA) operates in the near-field region. In our design, the transmitter concurrently delivers confidential data to information receivers and energy to zero-energy devices (ZEDs) via non-orthogonal multiple access (NOMA). A key innovation of our approach is the specialized near-field beamfocusing technique derived from a three-dimensional spherical channel model, which explicitly accounts for the unique propagation characteristics of near-field communications and distinguishes our method from traditional far-field designs. We formulate a non-convex optimization problem aimed at maximizing the secrecy rate while satisfying minimum quality-of-service and energy harvesting requirements. To solve this problem, we develop an iterative algorithm based on weighted sum-rate maximization and sequential convex approximations that effectively mitigate interference and enhance beamfocusing performance. Numerical simulations demonstrate that, with a 64-element uniform linear array and 40 dBm transmit power, our near-field NOMA system achieves an 18.41% higher secrecy rate than near-field spatial division multiple access (SDMA) and a 36.78-fold improvement over near-field orthogonal multiple access (OMA), along with a 6.39 dBm increase in harvested power relative to SDMA. These results underscore the critical role of specialized near-field design in next-generation 6G networks and its significant implications for industrial internet-of-things (IoT) and Industry 4.0 applications.

Leveraging inherent advantages of large bandwidth, high carrier frequency, and fine resolution, millimeter-wave (mmWave) technology is poised to play a pivotal role in integrated sensing and communication (ISAC) applications envisioned for sixth-generation (6G) networks. This paper proposes a stochastic geometry-based analytical framework to evaluate the performance of hybrid ISAC networks integrating sub-6 GHz and mmWave base stations (BSs). The framework explicitly incorporates band-specific propagation characteristics. Each mmWave BS is equipped with a large-scale antenna array to compensate for high-frequency propagation loss. Based on received signal power, we propose the maximum received echo signal power (Max-RESP) and maximum received average signal power (Max-RASP) association schemes to ensure that the target and user are associated with the BS providing better link conditions, respectively. Using stochastic geometry and probability theory, we derive analytical expressions for sensing distance accuracy and the communication achievable rate. The analytical results are validated via extensive Monte Carlo simulations. Numerical results show that hybrid sub-6 GHz and mmWave ISAC networks significantly outperform conventional pure sub-6 GHz networks and can approach the performance of pure mmWave networks by appropriately tuning the deployment density ratio. The appropriate density ratio provides practical guidance for balancing cost efficiency with performance enhancement. Moreover, the results reveal a performance bottleneck at higher density ratios, primarily due to the saturation of the signal-to-interference-plus-noise ratio (SINR). These findings highlight the crucial role of selecting an appropriate density ratio in hybrid sub-6GHz and mmWave ISAC networks.

In this paper, we investigate a reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) system, where the RIS is modeled using multiport network theory based on theZ-parameter representation. Unlike conventional RIS models based on reflection-coefficient matrices, we characterize RIS reconfigurability with tunable circuit impedances, thus capturing the electromagnetic mutual coupling (MC) effects among RIS elements. Specifically, we jointly optimize the transmit covariance matrix at the base station (BS) and the RIS tunable load impedance matrix to maximize the radar signal-to-noise ratio (SNR). The power budget at the BS and the quality of service (QoS) constraints for the communication users are also satisfied. To highlight the impact of electromagnetic MC on the system, both the no-MC and the MC-aware cases are considered. For the non-convex MC-aware problem, we propose an alternating optimization (AO) algorithm that integrates Neumann series approximation, semidefinite relaxation (SDR), and sequential rank-one constraint relaxation (SROCR) techniques. As a simplified form of the MC-aware case, the no-MC case can be considered as a sub-algorithm embedded in the proposed solution framework. Numerical results show that electromagnetic MC significantly affects the system performance, especially under sub-wavelength spacing.

In this paper, we investigate an integrated sensing and communication (ISAC) system assisted by a dynamic metasurface antenna (DMA), where the base station (BS) simultaneously communicates with multiple users and performs target sensing. Specifically, this paper aims to maximize the radar signal-to-noise ratio (SNR) at the BS by jointly optimizing the beamforming matrix and the DMA weight matrix, subject to signal-to-interference-plus-noise ratio (SINR) constraints for the users, the maximum transmit power at the BS and the Lorentzian constraint associated with the DMA elements. To tackle this non-convex optimization problem, an alternating optimization (AO) algorithm is proposed. In this algorithm, semidefinite relaxation (SDR) is employed to optimize the beamforming matrix, while sequential rank-one constraint relaxation (SRCR) is used to optimize the DMA weight matrix. Additionally, the penalty dual decomposition (PDD) and successive convex approximation (SCA) techniques are utilized as an alternative approach to solve the DMA weight matrix. Simulation results demonstrate that the DMA-assisted ISAC system achieves favorable results. The impact of different parameters on the objective value is analyzed, which shows that the PDD method outperforms the SRCR method.

In this paper, we propose a three-stage unified channel estimation strategy for reconfigurable intelligent surface (RIS)-aided multi-user (MU) multiple-input multiple-output (MIMO) millimeter wave (mmWave) systems with the existence of the direct channels, where the base station (BS), the users and the RIS are equipped with uniform planar array (UPA). The effectiveness of the developed three-stage strategy stems from the careful design of both the pilot signal sequence of the users and the vectors of RIS. Specifically, in Stage I, the cascaded channel components are eliminated by configuring the RIS phase shift vectors with a π difference to estimate the direct channels for all users. The orthogonal subspace projection is employed in Stage II to obtain equivalent signal matrices, enabling the estimation of angles of departure (AoDs) of the user-RIS channel for all users. In Stage III, we combine the signals of the time slots with the same pilots and project obtained measurement matrix to the orthogonal complement space of the component consisting of the portion of the direct channel, which removes the direct components and thus prevents error propagation from the direct channels to the cascaded channels. Then, we estimate the angles of arrival (AoAs) of the RIS-BS channel and remaining parameters of the cascaded channel for all users by exploiting the sparsity and correlation in the obtained equivalent matrices. Simulation results demonstrate that the proposed method yields better estimation performance than the existing methods.

Cell-free massive multiple-input multiple-output (CF massive MIMO) is regarded as a promising technology for next-generation wireless communication systems. However, relying on a single central processing unit (CPU) in CF massive MIMO systems is not scalable in practical networks, requiring the introduction of multiple CPUs for more efficient and feasible transmission. In this paper, we investigate a CF massive MIMO system with multiple CPUs. To obtain flexible and cost-efficient deployment, we propose to use wireless x-haul links instead of wired ones. More specifically, we assume that both the fronthaul links from the APs to the corresponding CPU and the backhaul links between CPUs operate under millimeter wave (mmWave) networks. Taking into account a tradeoff between the degree of centralized coordination and the signal overhead on the backhaul links, we consider four levels of multiple CPUs cooperation schemes from fully centralized to fully distributed. In addition, we propose a binary search method to allocate the backhaul capacities for maximizing the sum spectral efficiency (SE). Simulation results show that mmWave backhaul amplifies the compression noise introduced by mmWave fronthaul, leading to a more pronounced impact on the SE of systems. In this case, the centralized processing scheme can generate more compression noise due to the larger data overhead on the backhaul link, making the distributed processing scheme a superior processing scheme, especially when dealing with a large number of APs or significant distances between CPUs.

The rapid proliferation of mobile devices within Internet of Things (IoT) has substantially heightened the demand for mobile edge computing (MEC). Fog computing (FC) is a more advanced form of edge computing that allows computing nodes to cooperate with each other. Reconfigurable intelligent surfaces (RIS) have emerged as a critical technology for optimizing wireless communication environments, attracting considerable attention. In this paper, we develop an online optimization problem for RIS-aided mobile FC deployed across wireless networks with computing nodes at the base stations (BS). We propose a Lyapunov-drift-plus-penalty-based, space aggregation-assisted proximal policy optimization (LSAPPO) algorithm to tackle the challenges in online optimization problem in RIS-aided mobile FC system. Our technique integrates a reinforcement learning (RL) algorithm employing the proximal policy optimization (PPO) agent, further enhanced by Lyapunov drift-plus-penalty optimization. The space aggregation technique effectively consolidates excessive decision variables and channel state information (CSI) into a manageable set of parameters to streamline the computing framework. Numerical simulation result shows that our proposed algorithm surpasses the benchmarks, underscoring the effectiveness in complicated wireless networks. Furthermore, we introduce the multi-agent LSAPPO algorithm to address the distributed demands of practical scenarios. The multi-agent LSAPPO algorithm enhances convergence speed and performs better in large-scale problems.

In this paper, we analyze the role of fluid antenna systems (FAS) in multi-user systems with hardware impairments (HIs). Specifically, we investigate a scenario where a base station (BS) equipped with multiple fluid antennas communicates with multiple communication users (CUs), each equipped with a single fluid antenna. Our objective is to maximize the minimum communication rate among all users by jointly optimizing the BS's transmit beamforming, the positions of its transmit fluid antennas, and the positions of the CUs' receive fluid antennas. To address this non-convex problem, we propose a block coordinate descent (BCD) algorithm integrating semidefinite relaxation (SDR), rank-one constraint relaxation (SRCR), successive convex approximation (SCA), and majorization-minimization (MM). Simulation results demonstrate that FAS significantly enhances system performance and robustness, with notable gains when both the BS and CUs are equipped with fluid antennas. Even under low transmit power conditions, deploying FAS at the BS alone yields substantial performance gains. However, the effectiveness of FAS depends on the availability of sufficient movement space, as space constraints may limit its benefits compared to fixed antenna strategies. Our findings highlight the potential of FAS to mitigate HIs and enhance multi-user system performance, while emphasizing the need for practical deployment considerations.

We propose a novel integrated sensing and communication (ISAC) framework that combines active reconfigurable intelligent surfaces (RIS) with a movable antenna (MA) array at the base station to jointly enhance the communication throughput and the radar sensing resolution. Unlike conventional architectures that employ passive RIS or fixed antenna arrays, the proposed framework leverages dual-domain reconfigurability, electromagnetic and geometric, by jointly optimizing transmit beamforming, RIS reflection coefficients with amplification constraints, and the spatial positions of the mobile antennas. The system is modeled under a practical RIS noise amplification model and subject to the constraints of stringent signal-to-interference-plus-noise ratio (SINR), radar beampattern, and transmission power. A unified optimization problem is formulated and decomposed into tractable subproblems using an alternating optimization approach based on semidefinite relaxation (SDR), successive convex approximation (SCA), and convex programming. Numerical results confirm that the proposed design significantly outperforms conventional passive RIS and fixed array systems in terms of both radar and communication metrics, particularly under dynamic channel conditions and constrained power budgets.

Cooperated with simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and unmanned aerial vehicle (UAV), a non-orthogonal multiple access (NOMA) covert communication network is conceived. However, open channels are more vulnerable to eavesdropping byWardens, overlying that the channel state information (CSI) may be unstable with UAV’s time-varying deployments. In this paper, a deep reinforcement learning (DRL)-based and instantaneous channel-deducted framework is investigated for resolving the cutting-edge maximization problem of covert communication rate, subjected to the UAV flight, QoS requirement, and communication covertness. Given the channels instability caused by time-varying UAV flight, we design a channel deduction network by integrating complex-domain multi-layer perceptron (CMixer) and recurrence-based bidirectional long-short term memory (BiLSTM) to exploit the nonlinear correlations of channels in time, spatial location, and antenna domains. Relying on the states with deducted channels, the Twin Delayed Deep Deterministic policy gradient (TD3) as a proactive and policy-based DRL algorithm is used to iteratively train an agent responsible for adaptive adjusting UAV deployment and STAR-RIS beamforming. Simulation results demonstrate the effectiveness of the proposed channel deduction scheme, covert communication mechanism, and their synthesis.

The emergence of extremely large-scale antenna arrays (ELAA) in millimeter-wave (mmWave) communications, particularly in high-mobility scenarios, highlights the importance of near-field beam prediction. Unlike the conventional far-field assumption, near-field beam prediction requires codebooks that jointly sample the angular and distance domains, which leads to a dramatic increase in pilot overhead. Moreover, unlike the farfield case where the optimal beam evolution is temporally smooth, the optimal near-field beam index exhibits abrupt and nonlinear dynamics due to its joint dependence on user angle and distance, posing significant challenges for temporal modeling. To address these challenges, we propose a novel Convolutional Neural Network– Generative Pre-trained Transformer 2 (CNN–GPT2) based near-field beam prediction framework. Specifically, an uplink pilot transmission strategy is designed to enable efficient channel probing through widebeam analog precoding and frequency-varying digital precoding. The received pilot signals are preprocessed and passed through a CNN-based feature extractor, followed by a GPT-2 model that captures temporal dependencies across multiple frames and directly predicts the near-field beam index in an end-to-end manner. A pretraining–finetuning strategy is further adopted, where the model is first pretrained via masked prediction and then finetuned for the downstream beam prediction task, significantly improving training efficiency and accuracy. Simulation results under the 3GPP TR 38.901 channel model demonstrate that the proposed method achieves higher beam prediction accuracy than conventional recurrent models, while maintaining competitive performance in normalized beam-forming gain. These results confirm the feasibility of employing large-model AI for robust and low-overhead near-field beam management in future 6G systems.

This paper proposes a three-stage uplink channel estimation protocol for reconfigurable intelligent surface (RIS)-aided multi-user (MU) millimeter-wave (mmWave) multiple-input single-output (MISO) systems, where both the base station (BS) and the RIS are equipped with uniform planar arrays (UPAs). The proposed approach explicitly accounts for the mutual coupling (MC) effect, modeled via scattering parameter multiport network theory. In Stage~I, a dimension-reduced subspace-based method is proposed to estimate the common angle of arrival (AoA) at the BS using the received signals across all users. In Stage~II, MC-aware cascaded channel estimation is performed for a typical user. The equivalent measurement vectors for each cascaded path are extracted and the reference column is reconstructed using a compressed sensing (CS)-based approach. By leveraging the structure of the cascaded channel, the reference column is rearranged to estimate the AoA at the RIS, thereby reducing the computational complexity associated with estimating other columns. Additionally, the common angle of departure (AoD) at the RIS is also obtained in this stage, which significantly reduces the pilot overhead for estimating the cascaded channels of other users in Stage~III. The RIS phase shift training matrix is designed to optimize performance in the presence of MC and outperforms random phase scheme. Simulation results validate that the proposed method yields better performance than the MC-unaware and existing approaches in terms of estimation accuracy and pilot efficiency.

This paper investigates the two-timescale design for access point (AP) mode selection and power allocation to realize the full potential of the cooperative bi-static ISAC network with low system overhead, where the beamforming at the APs is adapted to the rapidly-changing instantaneous channel state information (CSI), while the AP mode selection and power allocation are adapted to the slowly-changing statistical CSI. Firstly, the minimum mean square error (MMSE) estimator is applied to estimate the channels between the APs and the channels between the APs and the user equipments (UEs). Then we adopt the low-complexity maximum ratio transmission (MRT) beamforming and maximum ratio combining (MRC) detector, and derive the closed-form expressions of the ergodic rate of the UEs and the sensing signal-to-interference-plus-noise-ratio (SINR). A non-convex mix integer optimization problem is formulated to maximize the minimum sensing SINR under the communication quality of service (QoS) constraints. McCormick envelope relaxation and successive convex approximation (SCA) techniques are applied to solve the challenging non-convex mix integer optimization problem. Extensive simulation results demonstrate the analytical accuracy of the closed-form expressions and validate the convergence and effectiveness of the proposed AP mode selection and power allocation scheme.

This paper studies an elasticity-enhanced uplink architecture that synergistically integrates the coordinated full-spectrum reuse in multi-cell cellular networks with multicarrier rate-splitting multiple access (RSMA). The uplink multi-layer RSMA granularly partitions each user’s data stream by strategically distributing splitted submessages across subcarriers and employing an elaborate decoding order, thereby fully exploiting the available spatial-spectral degrees of freedom. Particularly, the sum rate maximization for the multi-cell system is formulated through joint optimization of the user association, uplink power allocation, submessage-specific subcarrier assignment, receive beamforming, and decoding order. To tackle the problem’s non-convexity and mitigate the centralized computational burden, a two-stage approach is developed. First, a low-complexity base station (BS) selection method, grounded in matching games, is proposed to partition the user set. Next, a collaborative distributed scheme is proposed to delegate computational process to the corresponding BSs, where each BS independently addresses the remaining local problems using an alternating optimization algorithm. Specifically, the majorization-minimization (MM) and dual decomposition techniques are employed to derive the suboptimal solutions for the power and subcarrier allocation, while the fractional programming and alternating direction method of multipliers (ADMM) are utilized to achieve closed-form updating of the receive beamforming. Moreover, a dynamically optimized decoding order strategy is analytically derived. Simulation results validate the efficacy of the proposed algorithm in sum rate gain and computational complexity, showcasing that the RSMA-aided multi-cell collaborative transmission can attain superior performance than existing schemes.

In this work, we aim to effectively characterize the performance of cooperative integrated sensing and communication (ISAC) networks and to reveal how performance metrics relate to network parameters. To this end, we introduce a generalized stochastic geometry framework to model the cooperative ISAC networks, which approximates the spatial randomness of the network deployment. Based on this framework, we derive analytical expressions for key performance metrics in both communication and sensing domains, with a particular focus on communication coverage probability, radar information rate and coverage probability for sensing. The analytical expressions derived explicitly highlight how performance metrics depend on network parameters, thereby offering valuable insights into the deployment and design of cooperative ISAC networks. In the end, we validate the theoretical performance analysis through Monte Carlo simulation results. Our results demonstrate that increasing the number of cooperative base stations (BSs) significantly improves both metrics, while increasing the BS deployment density has a limited impact on communication coverage probability but substantially enhances the radar information rate. Additionally, increasing the number of transmit antennas is effective when the total number of transmit antennas is relatively small. The incremental performance gain reduces with the increase of the number of transmit antennas, suggesting that indiscriminately increasing antennas is not an efficient strategy to improve the performance of the system in cooperative ISAC networks.

Movable antennas (MAs) represent a novel approach that enables flexible adjustments to antenna positions, effectively altering the channel environment and thereby enhancing the performance of wireless communication systems. However, conventional MA implementations often adopt fully digital beamforming (FDB), which requires a dedicated RF chain for each antenna. This requirement significantly increase hardware costs, making such systems impractical for multi-antenna deployments. To address this, hardware-efficient analog beamforming (AB) offers a cost-effective alternative. This paper investigates the physical layer security (PLS) in an MA-enabled multiple-input single-output (MISO) communication system with an emphasis on AB. In this scenario, an MA-enabled transmitter with AB broadcasts common confidential information to a group of legitimate receivers, while a number of eavesdroppers overhear the transmission and attempt to intercept the information. Our objective is to maximize the multicast secrecy rate (MSR) by jointly optimizing the phase shifts of the AB and the positions of the MAs, subject to constraints on the movement area of the MAs and the constant modulus (CM) property of the analog phase shifters. This MSR maximization problem is highly challenging, as we have formally proven it to be NP-hard. To solve it efficiently, we propose a penalty constrained product manifold (PCPM) framework. Specifically, we first reformulate the position constraints as a penalty function, enabling unconstrained optimization on a product manifold space (PMS), and then propose a parallel conjugate gradient descent algorithm to efficiently update the variables. Simulation results demonstrate that MA-enabled systems with AB can achieve a well-balanced performance in terms of MSR and hardware costs.

In this paper, we investigate reconfigurable intelligent surface (RIS)-assisted communication systems which involve a fixed-antenna base station (BS) and a mobile user (MU) that is equipped with fluid antenna system (FAS). Specifically, the RIS is utilized to enable communication for the user whose direct link from the base station is blocked by obstacles. We propose a comprehensive framework that provides transmission design for both static scenarios with the knowledge of channel state information (CSI) and harsh environments where CSI is hard to acquire. It leads to two approaches: a CSI-based scheme where CSI is available, and a CSI-free scheme when CSI is inaccessible. Given the complex spatial correlations in FAS, we employ block-diagonal matrix approximation and independent antenna equivalent models to simplify the derivation of outage probabilities in both cases. Based on the derived outage probabilities, we then optimize the throughput of the FAS-RIS system. For the CSI-based scheme, we first propose a gradient ascent-based algorithm to obtain a near-optimal solution. Then, to address the possible high computational complexity in the gradient algorithm, we approximate the objective function and confirm a unique optimal solution accessible through a bisection search method. For the CSI-free scheme, we apply the partial gradient ascent algorithm, reducing complexity further than full gradient algorithms. We also approximate the objective function and derive a locally optimal closed-form solution to maximize throughput. Simulation results validate the effectiveness of the proposed framework for the transmission design in FAS-RIS systems.

Uplink integrated sensing and communication (ISAC) systems have recently emerged as a promising research direction, enabling simultaneous uplink signal detection and target sensing. In this paper, we propose the flexible projection (FP)-type receiver that unifies the projection-type receiver and the successive interference cancellation (SIC)-type receiver by using a flexible tradeoff factor to adapt to dynamically changing uplink ISAC scenarios. The FP-type receiver addresses the joint signal detection and target response estimation problem through two coordinated phases: 1) Communication signal detection using a reconstructed signal whose composition is controlled by the tradeoff factor, followed by 2) Target response estimation performed through subtraction of the detected communication signal from the received signal. With adjustable tradeoff factors, the FP-type receiver can balance the enhancement of the signal-to-interference-plus-noise ratio (SINR) with the reduction of correlation in the reconstructed signal for communication signal detection. The pairwise error probability (PEP) expressions are analyzed for both the maximum likelihood (ML) and the zero-forcing (ZF) detectors, revealing that the optimal tradeoff factor should be determined based on the adopted detection algorithm and the relative power of the sensing and communication (S&C) signals. A homotopy optimization framework is first applied for the FP-type receiver with a fixed tradeoff factor. This framework is then extended to develop the dynamic flexible projection (DFP)-type receiver, which iteratively adjusts the tradeoff factor for improved algorithm performance and environmental adaptability. Finally, we show that the length of the jointly processed signal should scale with the antenna size to fully unleash the potential of the uplink ISAC receiver.

Orbital angular momentum (OAM) technology can provide an additional degree of freedom in the spatial domain and solve the problem of spectrum shortage in the sixth generation (6G) networks. In this paper, a near-field OAM channel model based on the electromagnetic information (EMI) theory is proposed, in which the dyadic Green’s function approach and discrete Fourier basis functions are utilized to accurately describe the characteristics of near-field channel and OAM signal propagation in practical scenarios, respectively. To improve the performance of misaligned transmission, a near-field OAM misalignment precoding scheme is presented according to the characteristics of the proposed channel model. Specifically, phase errors are compensated and the optimal power is assigned according to the channel condition, which can mitigate the channel capacity loss caused by the the inter-mode interference resulting from the misalignment. Numerical results show that the benefit brought by the polarization along the propagation direction is predominantly significant at short distances, and decreases rapidly to zero as the distance increases. Results also show that the presented scheme outperforms the conventional fixed parameter scheme and effectively reduces the effects of the misalignment in practical scenarios.

The integration of dual-functional radar-communication (DFRC) systems with active reconfigurable intelligent surfaces (RIS) and movable antennas (MA) offers a powerful mechanism to meet the demands of sixth-generation (6G) networks. This paper presents a novel active RIS-aided integrated sensing and communication (ISAC) system where a DFRC base station (BS) equipped with a movable antenna array simultaneously serves multiple users and senses radar targets. The active RIS, incorporating low-power amplifiers, compensates for signal attenuation while reconfiguring the wireless environment. The MA array provides additional spatial degrees of freedom to enhance beam control, angular resolution, and robustness to blockage. To fully exploit this configuration, we formulate a joint beamforming and position optimization problem under SINR and power constraints. A successive convex approximation (SCA)-based alternating optimization algorithm is developed to address the non-convexities. Numerical results verify significant gains in communication throughput and radar beam sharpness compared to conventional passive RIS and fixed phased arrays, highlighting its potential for dynamic 6G scenarios such as intelligent surveillance and autonomous mobility.

Intelligent metasurfaces are emerging as a key technology for future wireless systems, enabling programmable control of electromagnetic wave propagation. Compared to conventional single-layer reconfigurable intelligent surfaces (RIS), stacked intelligent metasurfaces (SIM) introduce multiple reconfigurable layers to provide more flexible and precise beamforming. This paper investigates the integration of SIM into a downlink non-orthogonal multiple access (NOMA) system to improve spectral efficiency while maintaining low hardware complexity. The proposed system combines maximum ratio transmission (MRT) precoding at the base station, SIM-assisted analog beamforming, and NOMA-based power allocation. To maximize the system sum rate, we perform joint optimization of SIM phase shifts and user power levels through an alternating optimization (AO) framework, where each variable is updated iteratively while the other is held fixed. We evaluate three SIM-assisted strategies: NOMA, water-filling, and uniform power allocation. Simulation results demonstrate that the SIM-NOMA configuration achieves the 40% sum rate improvements, outperforming the other schemes while leveraging the low-cost wave-domain processing capabilities of SIM.

In this paper, we investigate a joint communication and computation resource allocation strategy for an unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system employing fluid antenna (FA). Specifically, each user is equipped with an FA to offload the entire computation tasks to the MEC server deployed on the UAV. By dynamically selecting antenna ports, users can achieve latency-efficient edge computing services, especially advantageous in dynamic environments. To minimize the maximum execution delay of all the users, we jointly optimize the UAV location, FA port selection, and computation resource allocation, subject to computational capacity constraints. The original non-convex optimization problem is decomposed into three tractable subproblems within a block coordinate descent (BCD) algorithm. The optimal computing frequencies are derived in closed form, while the UAV location and FA port selection are optimized using low-complexity iterative algorithms based on successive convex approximation (SCA) and linear programming (LP) techniques. In addition to conventional benchmarks with fixed-position antennas (FPAs), we also introduce a reconfigurable intelligent surface (RIS)-assisted system as a comparative baseline. Simulation results demonstrate that the proposed FA-assisted scheme significantly outperforms both FPAs and RIS-assisted counterparts, with performance gains becoming more pronounced in multi-task and highly dynamic scenarios, establishing FA-assisted UAV-MEC as a promising solution for future deployments.

Reconfigurable Intelligent Surface (RIS) is a key technology for radar and communication systems. This paper focuses on designing RIS-aided communication-compatible MIMO radar unimodular waveform design for radar and communication coexistence. The goal is to minimize the RIS-aided spatial Integrated Sidelobe Level Ratio (ISLR) under spectral constraint and unimodular constraints on both the waveform and RIS phase shifts. This is a challenging non-convex problem that existing methods cannot solve directly. We observe that the spectral constraint can be rewritten as a smooth non-negative function, and the Product Complex Circle Manifold (PCCM) naturally satisfies the unimodular constraints. Based on these insights, we propose an Inequality Constrained Product Manifold Optimization (ICPMO) framework. The spectral constraint is handled using a smooth penalty function, reformulating the problem as an unconstrained optimization on the PCCM. We then develop a Parallel Conjugate Gradient Descent (PCGD) algorithm without relaxing the objective. Simulations show our method reduces beam sidelobes by about 10 dB and improves energy distribution nulling compared to non-RIS methods.

In this paper, we investigate the channel estimation for mmWave multiple-input multiple-output-(MIMO) orthogonal frequency division multiplexing (OFDM) systems in high-mobility scenarios. By leveraging the low-rank nature of mmWave channels and the multidimensional characteristics of MIMO-OFDM signals across space, time, and frequency, the received signals are structured as a fourth-order tensor that fits a low-rank CANDECOMP/PARAFAC (CP) model. We propose an estimation of signal parameters via rotational invariance techniques (ESPRIT)type decomposition-based method to solve the CP decomposition, which exploits the Vandermonde structure of the factor matrix. The channel parameters are then estimated from the factor matrices. Simulation results show that our method outperforms existing benchmarks.

Projection-type receivers have recently emerged as a promising approach for uplink integrated sensing and communications (ISAC) systems, facilitating simultaneous uplink signal detection and target sensing. However, the signal detection problem within projection-type receivers faces challenges due to the high dimensionality and rank-deficiency of the equivalent channel matrix. To address this rank-deficiency issue, we introduce two novel variations to reconfigure the equivalent channel matrix: the Projection-Tikhonov (PT) receiver and the Projection-Orthogonal Multiple Access (P-OMA) transceiver. The PT receiver is specifically designed to mitigate the impact of rank-deficiency through regularization, while the P-OMA transceiver utilizes the independent columns of the equivalent channel matrix to transmit a reduced number of communication symbols. For signal detection with the reconfigured channel matrix, we demonstrate that while the linear decoding algorithm can be effectively computed for various projection-type receivers, it yields poor performance. Given the high dimensionality of the equivalent channel matrix, we propose an efficient iterative algorithm based on the extreme point pursuit (EXPP) framework, using a low-complexity linear detector as the initial point. Finally, simulation results validate the effectiveness of the proposed design.

In this paper, a three-phase joint direct and cascaded channel estimation strategy is proposed for reconfigurable intelligent surface (RIS)-aided multi-user (MU) multiple-input multipleoutput (MIMO) millimeter wave (mmWave) systems with the existence of the direct channels. The base station (BS), the users and the RIS are equipped with uniform planar array (UPA). The effectiveness of the devised three-phase strategy is contingent upon the meticulous design of the pilot signal sequence and the RIS phase shift vectors. Specifically, in Phase I, by reversing the RIS phase shift vectors, we remove the cascaded channel components to estimate the direct channels. In Phase II, we employ the orthogonal subspace projection to obtain equivalent signal matrices for the estimation of angles of departure (AoDs) of the user-RIS channel. In Phase III, we combine the signals of time slots with the same pilots and project the obtained measurement matrix to the orthogonal complement space of the component consisting of the portion of the direct channel, which removes the direct components and thus prevents error propagation from the direct channels to the cascaded channels. Then, we estimate the angles of arrival (AoAs) of the RIS-BS channel and the remaining parameters of the cascaded channels. Simulation results show that the proposed method outperforms existing methods.

In most existing works focusing on channel estimation for reconfigurable intelligent surface (RIS)-Aided MU-MIMO mmWave systems, Simultaneous Orthogonal Matching Pursuit (SOMP) method is used to estimate the angle of departure (AoD) at the users to convert the overall MIMO cascaded channel into multiple MISO cascaded channels for further estimation. However, it has poor performance when the number of antennas is small due to the strong correlation of atoms in the dictionary. This paper proposes a novel uplink two-phase channel estimation scheme with high accuracy in angle domain. Specifically, in the first phase, by carefully designing the precoding matrix, the 1-th sub-cascaded channel related to the 1 -th antenna is separated and estimated. In the second phase, by utilizing the invariance of angles and the linear correlation of gains, AoDs can be estimated based on the one-dimensional search method with high accuracy. Thus, all the remaining sub-cascaded channels can be calculated and combined into the overall MIMO cascaded channel. Simulation results demonstrate the superiority of the proposed algorithm.

This paper proposes an uplink two-timescale transmission scheme for a fluid antenna-enhanced multi-user multiinput multi-output system (MU-MIMO-FAS), where antenna positions are optimized based on statistical channel state information (CSI), and beamforming at the base station (BS) adapts to rapidly-varying instantaneous CSI. Using a Rician channel model with imperfect CSI, we employ the linear minimum mean square error (LMMSE) method for channel estimation and a maximal ratio combining (MRC) detector to derive a closed-form expression for the achievable rate. Subsequently, we formulate a minimum user rate maximization problem for antenna position design, subject to movement and spacing constraints, and utilize a genetic algorithm (GA) to solve this non-convex problem. Numerical results demonstrate that the proposed two-timescale MU-MIMO-FAS design significantly outperforms the traditional fixed-position antenna (FPA) system.

This paper investigates the resource allocation problem for multi-user wideband cooperative integrated sensing and communication (ISAC) networks based on orthogonal frequency-division multiplexing (OFDM) waveforms. In order to balance sensing and communication performance with limited spectrum resources in this wideband cell-free system, we aim to maximize the sum rate, encompassing both communication and radar rates, while adhering to constraints related to access point (AP) power and spectrum resources. We utilize alternate optimization (AO) methods to optimize power and spectrum resources separately. For power optimization, we employ the fractional programming (FP) algorithm to convert the problem into a convex one, which can be quickly solved by the primal-dual subgradient (PDS) method. As for subcarrier allocation optimization, we derive its closed-form solution. Simulation results indicate that the communication and sensing performance of the cell-free ISAC system outperforms that of the conventional centralized ISAC system.

In future networks, the integrated sensing, communication and computation (ISCC) has gradually become a research hotspot. In this paper, we investigate a novel computation resource allocation scheme for reconfigurable intelligent surfaces (RIS) backscatter communication (BackCom)-aided ISCC system. We consider the joint design of transmit beamforming at BS and the reflecting coefficients at RIS as well as the computation resource allocation of each user. The optimization problem for the max-min computation efficiency (CE) under the constraints of power consumption, the Cramér-Rao bound (CRB) for angles estimation and communication requirement of each user is formulated. To deal with the intractable optimization problem, the block coordinate descent (BCD) algorithm is utilized to tackle the joint optimization problem. We propose the penalty function-based successive convex approximation (SCA) method to optimize the reflecting coefficients and the majorization-minimization (MM) framework to design the transmit beamforming, respectively. In addition, considering the high complexity of the proposed SCA based algorithm, we design a low-complexity beamforming and reflection coefficient scheme for a special case of single target scenario. Simulation results show that the introduction of RIS-BackCom can improve the efficiency of computing and maintain the tradeoff between CE and sensing performance.

Fluid antenna (FA) is capable of achieving a significant degree of spatial diversity within the limited space of a wireless device by adjusting the radiating elements to optimal positions. In this article, we explore the potential of deploying FAs on the overall performance of wireless powered communication network (WPCN). Specifically, each Internet of Things (IoT) device in WPCN is equipped with a single FA comprising multiple ports. The IoT device (ID) selects the optimal receive port for energy harvesting from the power beacon (PB), followed by choosing the optimal transmit port to send its data to the access point (AP). Our objective is to maximize the sum throughput of IDs by jointly optimizing port activation and time scheduling, subject to constraints on the received signal-to-noise ratio (SNR) of each individual ID and the total transmission time. To tackle this nonconvex problem, we first apply the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions to find the optimal solutions for time slots. Then, we introduce an efficient algorithm based on the alternating optimization (AO) method to iteratively achieve a locally optimal solution for port activation. Additionally, a low-complexity scheme is proposed to minimize computational overhead. Simulation results reveal that incorporating FAs into a WPCN markedly improves the overall system performance, and highlights the benefits of port selection for the FA in comparison to baseline methods.

This paper investigates a multi-user uplink mobile edge computing (MEC) network, where the users offload partial tasks securely to an access point under the non-orthogonal multiple access policy with the aid of a reconfigurable intelligent surface (RIS) against a multi-antenna eavesdropper. We formulate a non-convex optimization problem of minimizing the total energy consumption subject to secure offloading requirement, and we build an efficient block coordinate descent framework to iteratively optimize the number of local computation bits and transmit power at the users, the RIS phase shifts, and the multi-user detection matrix at the access point. Specifically, we successively adopt successive convex approximation, semi-definite programming, and semidefinite relaxation to solve the problem with perfect eavesdropper’s channel state information (CSI), and we then employ S-procedure and penalty convex-concave to achieve robust design for the imperfect CSI case. We provide extensive numerical results to validate the convergence and effectiveness of the proposed algorithms. We demonstrate that RIS plays a significant role in realizing a secure and energy-efficient MEC network, and deploying a well-designed RIS can save energy consumption by up to 60% compared to that without RIS. We further reveal impacts of various key factors on the secrecy energy efficiency, including RIS element number and deployment position, user number, task scale and duration, and CSI imperfection.

The rapid development of generative Artificial Intelligence (AI) continually unveils the potential of Semantic Communication (SemCom). However, current talking-face SemCom systems still encounter challenges such as low bandwidth utilization, semantic ambiguity, and diminished Quality of Experience (QoE). This study introduces a Large Generative Model-assisted Talking-face Semantic Communication (LGM-TSC) System tailored for talking-face video communication. Firstly, we introduce a Generative Semantic Extractor (GSE) at the transmitter based on the FunASR model to convert semantically sparse talking-face videos into text with high information density. Secondly, we establish a private Knowledge Base (KB) based on the Large Language Model (LLM) for semantic disambiguation and correction, complemented by a joint knowledge base-semantic-channel coding scheme. Finally, at the receiver, we propose a Generative Semantic Reconstructor (GSR) that utilizes BERT-VITS2 and SadTalker models to transform text back into a high-QoE talking-face video matching the user’s timbre. Simulation results demonstrate the feasibility and effectiveness of the proposed LGM-TSC system.

Orthogonal time frequency space (OTFS) modulation, a delay-Doppler (DD) domain communication scheme exhibiting strong robustness against the Doppler shifts, has the potentials to be employed in LEO satellite communications. However, the performance comparison with the orthogonal frequency division multiplexing (OFDM) modulation and the resource allocation scheme for multiuser OTFS-based LEO satellite communication system have rarely been investigated. In this paper, we conduct a performance comparison under various channel conditions between the OTFS and OFDM modulations, encompassing evaluations of sum-rate and bit error ratio (BER). Additionally, we investigate the joint optimal allocation of power and delay-Doppler resource blocks aiming at maximizing sum-rate for multiuser downlink OTFS-based LEO satellite communication systems. Unlike the conventional modulations relying on complex input-output relations within the Time-Frequency (TF) domain, the OTFS modulation exploits both time and frequency diversities, i.e., delay and Doppler shifts remain constant during a OTFS frame, which facilitates a DD domain input-output simple relation for our investigation. We transform the resulting non-convex and combinatorial optimization problem into an equivalent difference of convex problem by decoupling the conditional constraints, and solve the transformed problem via penalty convex-concave procedure algorithm. Simulation results demonstrate that the OTFS modulation is robust to carrier frequency offsets (CFO) caused by high-mobility of LEO satellites, and has superior performance to the OFDM modulation. Moreover, numerical results indicate that our proposed resource allocation scheme has higher sum-rate than existing schemes for the OTFS modulation, such as delay divided multiple access and Doppler divided multiple access, especially in the high signal-to-noise ratio (SNR) regime.

We propose a novel integrated sensing and communication (ISAC) scheme that leverages sensing to assist communication in light-of-sight (LoS) environments, ensuring fast initial access, seamless user tracking, and uninterrupted communication for millimeter wave (mmWave) wideband systems. True-time-delayers (TTDs) are utilized to generate frequency-dependent radar rainbow beams by controlling the beam squint effect. These beams cover users across the entire angular space simultaneously for fast beam training using just one orthogonal frequency-division multiplexing (OFDM) symbol. Three detection and estimation schemes are proposed based on radar rainbow beams for estimation of the users’ directions, distances, and velocities, which are then exploited for communication beamformer design. The first proposed scheme utilizes a single-antenna radar receiver and one set of rainbow beams, but may cause a Doppler ambiguity. To tackle this limitation, two additional schemes are introduced, utilizing two sets of rainbow beams and a multi-antenna receiver, respectively. Furthermore, the proposed detection and estimation schemes are extended to realize user tracking by choosing different subsets of OFDM subcarriers. This approach eliminates the need to switch phase shifters and TTDs, which is typically required for existing tracking schemes. Simulation results reveal the effectiveness of the proposed rainbow beam-based training and tracking methods for mobile users.

This paper proposes a novel channel estimation protocol for a reconfigurable intelligent surface (RIS) aided multi-user (MU) multi-input multi-output (MIMO) millimeter wave (mmWave) system under the hybrid architecture where the direct channels between the base station (BS) and user equipment (UE) exist. There are two stages respectively estimating the direct and cascaded channels. In Stage I, besides the direct channels, the angles of arrival (AoA) and the angles of departure (AoD) of the cascaded channels are also estimated. Stage II is divided into two sub-stages and the overall cascaded channels are estimated. In sub-stage I, the cascaded channel of a typical UE is estimated. In sub-stage II, the cascaded channels of all the remaining UEs are estimated. Simulation results demonstrate that the proposed method has lower pilot overhead and achieves higher accuracy than the existing benchmark approaches.

Extremely large-scale multiple-input multiple-output (XL-MIMO) systems are capable of improving spectral efficiency by employing far more antennas than conventional massive MIMO at the base station (BS). However, beam training in multiuser XL-MIMO systems is challenging. Firstly, new near-field channel models and near-field XL-MIMO transmit beamforming (TBF) codebooks have to be adopted due to the dramatic increase in the number of antennas, which results in an excessive pilot overhead for beam training. Secondly, when the user density is high, the wireless propagation environments of the adjacent users are similar and hence the pilot signals received by the BS from different users appear to be interrelated, which is potentially beneficial but difficult to exploit. Thirdly, different users might share the same beam-direction, which causes excessive inter-user interference. To tackle these issues, we conceive a three-phase graph neural network (GNN)-based beam training scheme for multiuser XL-MIMO systems. In the first phase, only far-field wide beams have to be tested for each user and the GNN is utilized to map the beamforming gain information of the far-field wide beams to the best available near-field codeword for each user. In addition, the proposed GNN-based scheme can exploit the position-correlation between adjacent users for further improvement of the accuracy of beam training. In the second phase, a beam allocation scheme based on the probability vectors produced at the outputs of GNNs is proposed to address the above beam-direction conflicts between users. In the third phase, the hybrid TBF is designed for further reducing the inter-user interference. Our simulation results show that the proposed scheme significantly improves beam training accuracy and reduces pilot overhead compared to traditional neural network-based benchmarks. Hence it is more suitable for multiuser XL-MIMO systems. Moreover, the performance of the proposed beam training scheme approaches that of an exhaustive search, despite requiring only about 7% of the pilot overhead.

Combining millimetre-wave (mmWave) communications with an extremely large-scale antenna array (ELAA) presents a promising avenue for meeting the spectral efficiency demands of future sixth-generation (6G) mobile communications. This technology achieves a high data rate and establishes high-gain directional transmission links. However, beam training for mmWave ELAA systems is challenged by excessive pilot overheads as well as insufficient accuracy, as the huge near-field codebook has to be accounted for. In this paper, inspired by the similarity between far-field sub-6 GHz channels and near-field mmWave channels, we propose to leverage sub-6 GHz uplink pilot signals to directly estimate the optimal near-field mmWave codeword, which aims to reduce pilot overhead and bypass the channel estimation. Moreover, we adopt deep learning to perform this dual mapping function, i.e., sub-6 GHz to mmWave, far-field to near-field, and a novel neural network structure called NMBEnet is designed to enhance the precision of beam training. Specifically, when considering the orthogonal frequency division multiplexing (OFDM) communication scenarios with high user density, correlations arise both between signals from different users and between signals from different subcarriers. Accordingly, the convolutional neural network (CNN) module and graph neural network (GNN) module included in the proposed NMBEnet can leverage these two correlations to further enhance the precision of beam training. To better evaluate the performance of the proposed algorithm, we employ state-of-the-art system simulation software to obtain realistic channel data. Simulation results demonstrate the superior performance of the proposed strategy compared to the exhaustive search scheme and existing deep learning-based schemes.

The paper evaluates the ergodic sum capacity, outage performance for communication users, and the detection probability, beampattern gain for sensing targets in a simultaneous transmitting and reflecting intelligent reflecting surfaces (STAR-IRS) aided integrated sensing and communication (ISAC) system. The rate splitting multiple access (RSMA) technique and maximal ratio transmit beamforming at the multi-antenna base station have been explored. Closed-form expressions for the ergodic sum capacity and outage probability of the STAR-IRS-aided RSMA ISAC system are derived through moment methods. The derived expressions are validated through Monte Carlo simulations. Additionally, to provide deeper insights into the diversity orders of the RSMA ISAC system, we conduct an asymptotic outage probability analysis in the high signal-to-noise ratio regime. The effect of the number of base station antennas and STAR-IRS elements on outage performance has been demonstrated, along with an explanation of the underlying reasons through diversity gain. Furthermore, it shows that the implementation of STAR-IRS significantly boosts the system’s ergodic sum capacity compared to traditional reflecting-only IRS. Additionally, the RSMA technique delivers more substantial performance improvements than the non-orthogonal multiple access (NOMA) in high transmit SNR conditions while demonstrating comparable performance in low transmit SNR scenarios. A comparison between energy splitting and mode switching STAR-IRS has been conducted under both ideal and random phase shift conditions. A trade-off analysis between communication and sensing rates is presented. Additionally, the accuracy of target sensing is evaluated by measuring the mean square error (MSE) in beampattern gain matching. The impact of quantization levels for phase shift of STAR-IRS on outage probability has also been addressed. Finally, the effects of power allocation for sensing on detection probability and beam pattern gain are also presented.

This paper investigates simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted multi-user downlink (dl) communications with a primary focus on maximizing information secrecy by considering the channel state information (CSI) error. Acquiring perfect CSI is particularly challenging due to the unavailability of radio frequency chains at the STAR-RIS, the inherent impact of noise and interference on the CSI estimation, as well as non-collaborative nature of the eavesdroppers. In particular, we tackle the worst-case robust beamforming design problem to maximize the sum secrecy rate of the system while considering transmit power limitations, quality of service requirements, and practical constraints on the STAR-RIS phase shifter array. To tackle the resulting non-convex problem, we employ the S-procedure as an initial step to approximate semi-infinite inequality constraints. Subsequently, we leverage the alternating optimization with a line search framework to update the precoder and phase shift matrix iteratively. Furthermore, we extend our solution to address the non-convexity by leveraging a deep reinforcement learning (DRL) multi-agent (MA) framework based on Markov decision process. We also analyze practical phase shifts and the effect of direct links to showcase the practicality of our approach. Simulation results confirm STAR-RIS’s significant performance edge, exhibiting approximately 27.1% higher secrecy in conventional optimization and around 35.4% in the MA-DRL context compared over the conventional RIS. Moreover, our proposed MA-DRL approach surpasses single-agent schemes by about 8.6% in the case of proximal policy optimization and 19.9% in the case of deep deterministic policy gradient, emphasizing the benefits of the MA framework with STAR-RIS.

In this paper, we propose a three-stage high-accuracy uplink channel estimation scheme that utilizes super-resolution algorithms for reconfigurable intelligent surface (RIS)-aided multi-user (MU) millimeter-wave (mmWave) multiple-input single-output (MISO) systems. The proposed protocol enhances both estimation accuracy and pilot overhead efficiency. In Stage I, we derive the covariance matrix of the received signal and estimate the common angles-of-arrival (AoAs) at the base station (BS) using super-resolution algorithms. In Stage II, we construct an equivalent multi-snapshot received signal matrix to estimate the cascaded angles-of-departure (AoDs) at the RIS for a typical user. This takes advantage of the invariance of angle information across multiple channel coherence blocks while accounting for varying channel gains. To further reduce noise impact, we apply the minimum mean square error (MMSE) criterion. The full channel state information (CSI) of the typical user is then estimated using a combination of super-resolution algorithms for angle estimation and the least squares (LS) method for gain estimation. In Stage III, we reconstruct the common BS-RIS channel and use the results from Stages I and II to estimate the full CSI for other users, significantly reducing pilot overhead. Simulation results demonstrate that the proposed method outperforms the existing approaches in terms of both angle estimation accuracy and overall performance, while maintaining the same pilot overhead.

Ultra-reliable low-latency communication (URLLC) is a critical type of service that imposes stringent latency requirements. Considering the random and burst URLLC packets arrival characteristics, incorporating spatial frequency reuse into multi-cell networks can significantly improve the system performance. Nevertheless, how to design the frequency refuse strategy for such a multi-cell URLLC system remains technically challenging. In this article, we investigate an online dynamic resource scheduling problem in a multi-cell downlink system with URLLC services. The long-term time-averaged effective throughput is maximized while guaranteeing the instantaneous transmission reliability and prolonged network stability. The formulated problem is a mixed integer nonlinear stochastic optimization problem, in which the Lyapunov optimization is first leveraged to transform the long-term maximization problem into sequential short-term online ones. To tackle the deterministic problem in each time-slot, we further propose a distributed algorithm that delegates computational processes to corresponding base stations for collaborative execution. In this framework, the user association is abstracted as a cooperative game model. Subsequently, each base station exploits alternating optimization and convex optimization approximation algorithms to address the remaining resource allocation problem. Simulation results validate the effectiveness of the proposed algorithm in throughput-backlog trade-off, showcasing that the multi-cell collaborative transmission can attain better performance compared with existing schemes.

In the Internet of vehicles, using jammers to assist covert communication can protect the security of vehicular communication in some sensitive or special scenarios. However, the issue of three-party spectrum resource allocation involving multiple coexisting transmitters, receivers, and jammers has rarely been studied. This paper investigates jammer selection and power control in vehicular covert communication. To maximize the utility of spectrum resources, a vehicular covert communication auction model (VCCAM) is established and formulated as a 0-1 integer programming problem. We derive the optimal detection threshold for minimum average detection error probability and corresponding optimal transmit power based on the slow fading statistical channel state information (CSI) of mobile links. We then propose a reverse Vickrey-Clarke-Groves (VCG) auction algorithm that ensures incentive compatibility and individual rationality. However, it lacks computational efficiency. To address this, we introduce the reverse second-price sealed-bid algorithm (SPSA), which reduces the VCG auction’s factorial complexity to polynomial complexity, offering a computationally efficient, suboptimal solution. Extensive simulations verify the effectiveness, communication covertness, computational efficiency, incentive compatibility, and individual rationality of the proposed algorithms, showing that our algorithm significantly outperforms other baselines.

This paper considers a movable antenna (MA)-aided secure multiple-input multiple-output (MIMO) communication system consisting of a base station (BS), a legitimate information receiver (IR) and an eavesdropper (Eve), where the BS is equipped with MAs to enhance the system’s physical layer security (PLS). Specifically, we aim to maximize the secrecy rate (SR) by jointly optimizing the transmit precoding (TPC) matrix, the artificial noise (AN) covariance matrix and the MAs’ positions under the constraints of the maximum transmit power and the minimum spacing between MAs. To solve this non-convex problem with highly coupled optimization variables, the block coordinate descent (BCD) method is applied to alternately update the variables. Specifically, we first reformulate the SR into a tractable form, and derive the optimal TPC matrix and the AN covariance matrix with fixed MAs’ positions by applying the Lagrangian multiplier method in semi-closed forms. Then, the majorization-minimization (MM) algorithm is employed to iteratively optimize each MA’s position while keeping others fixed. We also extend this work to the more general multicast scenario. Finally, simulation results are provided to demonstrate the effectiveness of the proposed algorithms and the significant advantages of the MAs over conventional fixed position antennas (FPAs) in enhancing system’s security.

This paper studies a comprehensive framework for reconfigurable intelligent surface (RIS)-assisted integrated communication, sensing, and computation (ICSC) systems. To satisfy the critical need for low-latency sensing, we formulate a weighted latency minimization problem encompassing both multi-user equipment (UE) and simplified single-UE scenarios. To address the formulated non-convex problem in the multi-UE scenario, we decouple the original problem into two subproblems, where the computational and beamforming settings are optimized alternately. Specifically, for the computational settings, we derive a closed-form solution for the offloading volume and propose a low-complexity algorithm based on the bisection search method to optimize the edge computing resource allocation. Additionally, we employ two equivalent transformations to address the challenge posed by the non-convex sum-of-ratios form in the objective function (OF) of the subproblem related to active and passive beamforming. Several techniques are then combined to address these subproblems. To bridge the gap between theoretical assumptions and practical deployments, a robust design extension accounting for imperfect channel state information (CSI) is developed using statistical error modeling. Furthermore, a low-complexity algorithm that offers closed-form solutions is developed for the simplified single UE scenario. Finally, simulation results substantiate the effectiveness of the proposed framework.

Time-varying channels are significantly influenced by the Doppler effect, which leads to rapid changes in channel gain and requires Doppler frequency estimation to compensate for channel phase shifts and improve communication quality. In this paper, we propose a novel fifth-generation (5G) new radio (NR) orthogonal frequency division multiplexing (OFDM)-based transmission structure for time-varying channel estimation in high-mobility scenarios. By designing an appropriate subcarrier spacing and slot format, we ensure that the pilot signals remain nearly invariant within a single slot and exhibit rotational invariance between different slots. Leveraging this rotational invariance, we introduce a novel algorithm based on Vandermonde-structured tensor decomposition, which is non-iterative and has lower computational complexity and higher robustness than other tensor-based algorithms. Moreover, we provide a theoretical analysis of the uniqueness condition of tensor decomposition, proving that the proposed algorithm has strong feasibility and requires low pilot overhead. We also analyze the mean square errors (MSEs) of the parameter estimates and present a concise derivation of the Cramér-Rao Bound (CRB). The results demonstrate that the proposed algorithm significantly outperforms compressed sensing (CS)-based methods and other tensor-based methods in terms of parameter estimation performance at medium to high SNR. Furthermore, the proposed algorithm, based on the instantaneous channel model, offers higher channel estimation accuracy than the Kalman filtering-based algorithm, which relies on statistical channel models. Simulation results with channel data generated by Wireless InSite, which constructs a real-world scattering environment, demonstrate the high estimation accuracy of the proposed algorithm, validating its effectiveness in practical scenarios.

Reconfigurable intelligent surfaces (RISs) hold significant promise for enhancing physical layer security (PLS). However, conventional RISs are typically modeled using diagonal scattering matrices, capturing only independent reflections from each reflecting element, which limits their flexibility in channel manipulation. In contrast, beyond-diagonal RISs (BD-RISs) employ non-diagonal scattering matrices enabled by active and tunable inter-element connections through a shared impedance network. This architecture significantly enhances channel shaping capabilities, creating new opportunities for advanced PLS techniques. This paper investigates PLS in a multiple-input multiple-output (MIMO) system assisted by BD-RISs, where a multi-antenna transmitter sends confidential information to a multi-antenna legitimate user while a multi-antenna eavesdropper attempts interception. To maximize the secrecy rate (SR), we formulate it as a non-convex optimization problem by jointly optimizing the transmit beamforming and BD-RIS REs under power and structural constraints. To solve this problem, we first introduce an auxiliary variable to decouple BD-RIS constraints. We then propose a low-complexity penalty product Riemannian conjugate gradient descent (P-PRCGD) method, which combines the augmented Lagrangian (AL) approach with the product manifold gradient descent (PMGD) method to obtain a Karush-Kuhn-Tucker (KKT) solution. Simulation results confirm that BD-RIS-assisted systems significantly outperform conventional RIS-assisted systems in PLS performance.

This paper characterizes the physical layer security (PLS) in a network utilizing massive multiple-input multiple-output (MIMO) for over-the-air computation (AirComp). When the direct links between the access point (AP) and the sensors are blocked, an intelligent reflecting surface (IRS) is employed to establish communication. Furthermore, the AP sends artificial noise (AN) to the eavesdropper to prevent wiretapping. We study the problem of minimizing the mean-square-error (MSE) between the original and intercepted signals subject to the transmit power constraints at the AP and the sensors, as well as how the MSE threshold hinders the eavesdropper under both perfect and imperfect channel state information (CSI). In the case of perfect CSI, obtaining a globally optimal solution for the investigated non-convex problem is challenging due to the optimization variables’ couple nature. Hence, we convert the problem into two sub-problems to obtain locally optimal solutions. One sub-problem can be solved by an exact penalty-based algorithm, while the other has a closed-form solution using the popular majorization-minimization (MM) algorithm. For the imperfect CSI, the robust beamforming optimization problem formulated is still non-convex. To address this, we harness the block coordinate descent (BCD) algorithm for alternately optimizing the variables to solve it. The results of our simulations demonstrate that the superior MSE performance exhibited by the proposed scheme.

The integration of sensing capabilities into communication systems, by sharing physical resources, has a significant potential for reducing spectrum, hardware, and energy costs while inspiring innovative applications. Cooperative networks, in particular, are expected to enhance sensing services by enlarging the coverage area and enriching sensing measurements, thus improving the service availability and accuracy. This paper proposes a cooperative integrated sensing and communication (ISAC) framework by leveraging information-bearing orthogonal frequency division multiplexing (OFDM) signals transmitted by access points (APs). Specifically, we propose a two-stage scheme for target localization, where communication signals are reused as sensing reference signals based on the system information shared at the central processing unit (CPU). In Stage I, we propose a two-dimensional fast Fourier transform (2D-FFT)-based algorithm to measure the ranges of scattered paths induced by targets, through the extraction of delay and Doppler information from the sensing channels between APs. Then, the target locations are estimated in Stage II based on these range measurements. Considering the potential occurrence of ill-conditioned measurements with large error during the extraction of time-frequency information, we propose an efficient algorithm to match the range measurements with the targets while eliminating ill-conditioned measurements, achieving high-accuracy target localization. In addition, based on the transmission configurations defined in the fifth generation (5G) standards, we elucidate the performance trade-offs in both communication and sensing, and extend the proposed sensing scheme for general scenarios. Finally, numerical results confirm the effectiveness of our sensing scheme and the cooperative gain of the ISAC framework.

Polarization adds an additional dimension to the radar signals, contributing to waveform diversity. Codesign of unimodular waveforms and filters with polarimetric power allocation for maximizing the signal-to-interference-plus-noise ratio (SINR) plays a key role in the polarimetric radar system. The problem is challenging to solve due to the nonconvex nature of the objective function and constraints, coupled with the interdependence of multiple variables. Existing methods mainly solve this problem by fixing the power allocation or relaxing the objective function and obtaining the receive filters with matrix inversion. We directly address this problem without matrix inversion by using the proposed adaptive unified manifold optimization (AUMO) framework. Specifically, a unified manifold space (UMS) is constructed to satisfy the constraints of unimodular waveform, filters, and power, transforming the problem to an unconstrained optimization problem over the manifold. To solve this problem, a parallel conjugate gradient (PCG) algorithm is derived. This algorithm can adaptively change the step size by exploring the local features of the manifold space. The experimental results based on the measured data show that the proposed method outperforms existing methods in terms of SINR gain and execution time.

The allocation of secure resources in non-orthogonal multiple access (NOMA) systems has gained significant recognition as a vital research focus in the realm of the Internet of Things (IoT). Previous studies have overlooked the security challenges associated with integrating NOMA with finite blocklength (FBL) transmission. Therefore, this paper examines a secure downlink NOMA system utilizing FBL transmission, which includes a base station (BS), a near user, a far user, and an external eavesdropper. We develop an optimization problem with the objective of maximizing the near user’s effective secrecy throughput, considering the secrecy rates, decoding error probabilities (DEPs), and effective secrecy throughput for both users. Notably, by meticulously defining the DEPs of the users as optimization variables, the monotonicity and concavity of these DEPs in relation to the blocklength, transmission power, and transmission rate can be established effectively. The problem is divided into two sub-problems focusing on the essential conditions for the secrecy rate of the near user, especially in scenarios where successive interference cancellation (SIC) is unsuccessful. These sub-problems are addressed using the block coordinate descent (BCD) algorithm and an exact penalty method. For comparison, the BCD algorithm is also applied to solve the optimization problem using the orthogonal multiple access (OMA) scheme. Numerical simulations confirm the effectiveness of our proposed approaches in improving secure resource allocation when NOMA is combined with FBL transmission.

This paper investigates a reconfigurable intelligent surface (RIS)-aided wideband massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system with low-resolution analog-to-digital converters (ADCs). Frequency-selective Rician fading channels are considered, and the OFDM data transmission process is presented in time domain. This paper derives the closed-form approximate expression of the uplink achievable rate, based on which the asymptotic system performance is analyzed when the number of the antennas at the base station and the number of reflecting elements at the RIS grow to infinity. Besides, the power scaling laws of the considered system are revealed to provide energy-saving insights. Furthermore, this paper proposes a gradient ascent-based algorithm to design the phase shifts of the RIS for maximizing the minimum user rate. Finally, numerical results are presented to verify the correctness of analytical conclusions and draw insights.

Existing works mainly rely on the far-field planar-wave-based channel model to assess the performance of reconfigurable intelligent surface (RIS)-enabled wireless communication systems. However, when the transmitter and receiver are in near-field ranges, the investigation of the channel statistics based on the planar-wave-based model will result in relatively low computing accuracy. To tackle this challenge, we initially develop an analytical framework for sub-array partitioning. This framework divides the large-scale RIS array into multiple sub-arrays, effectively reducing modeling complexity while maintaining acceptable accuracy. Then, we develop a beam domain channel model based on the proposed sub-array partition framework for large-scale RIS-enabled unmanned aerial vehicle (UAV)-to-vehicle communication systems, which can be used to efficiently capture the sparse features of RIS-enabled UAV-to-vehicle channels in both near-field and far-field ranges. Furthermore, some important propagation characteristics of the proposed channel model, including the spatial cross-correlation functions (CCFs), temporal auto-correlation functions (ACFs), frequency correlation functions (FCFs), channel capacities, and path loss statistics with respect to the different physical features of the RIS array and non-stationary properties of the channel model are derived and analyzed. Finally, simulation results are provided to demonstrate that the proposed framework is helpful to achieve a good tradeoff between the modeling complexity and accuracy for investigating the channel propagation characteristics, and therefore providing highly-efficient communications in RIS-enabled air-ground wireless networks.

Semantic Communication (SC) has emerged as a novel communication paradigm in recent years. Nevertheless, extant Image Semantic Communication (ISC) systems face several challenges in dynamic environments, including low information density, catastrophic forgetting, and uncertain Signal-to-Noise Ratio (SNR). To address these challenges, we propose a novel Vision-Language Model-based Cross-modal Semantic Communication (VLM-CSC) system. The VLM-CSC comprises three novel components: 1) Cross-modal Knowledge Base (CKB) is used to extract high-density textual semantics from the semantically sparse image at the transmitter and reconstruct the original image based on textual semantics at the receiver. The transmission of high-density semantics contributes to alleviating bandwidth pressure; 2) Memory-assisted Encoder and Decoder (MED) employ a hybrid long/short-term memory mechanism, enabling the semantic encoder and decoder to overcome catastrophic forgetting in dynamic environments when there is a drift in the distribution of semantic features; 3) Noise Attention Module (NAM) employs attention mechanisms to adaptively adjust the semantic coding and the channel coding based on SNR, ensuring the robustness of the CSC system. The experimental simulations validate the effectiveness, adaptability, and robustness of the CSC system.

As a recently proposed reconfigurable intelligent surface (RIS) architecture, active RIS has drawn considerable interest. The important feature of the active RIS is its ability to strengthen the impinging signals to mitigate the multiplicative fading effect inherent in passive RIS-aided systems. Herein, we explore the performance of an active RIS-aided uplink multi-user massive multiple-input multiple-output (MIMO) system, considering the phase noise at the RIS. Furthermore, a two-timescale scheme is utilized, where the base station (BS) beamforming is designed based on the instantaneous aggregated channel state information (CSI), while the statistical CSI is used for designing the phase shifts of the active RIS. In addition, the linear minimum mean square error (LMMSE) estimator is adopted to estimate the aggregated channel, which combines both the cascaded and direct channels. According to the estimated channel, a closedform expression for the lower bound of achievable rate is derived. Based on the theoretical expressions, the power scaling laws for the considered system are also investigated. Specifically, when each user’s transmit power is proportionally reduced by the quantity of BS antennasMor RIS elementsN, we find that the amplified thermal noise causes the lower bound of the achievable rate to approach zero asMorNtends to infinity. Moreover, an optimization approach based on a genetic algorithm (GA) is introduced to obtain the optimal phase shifts for maximizing the achievable rate. Numerical results reveal that the active RIS can greatly enhance the performance of the massive MIMO system compared to its passive counterpart.

This paper proposes a cooperative integrated sensing and communication (ISAC) scheme for low-altitude sensing scenario, aiming at estimating the parameters of the uncrewed aerial vehicles (UAVs) and enhancing the sensing performance via cooperation. The proposed scheme consists of two stages. In Stage I, we formulate the monostatic parameter estimation problem via using a tensor decomposition model. By leveraging the Vandermonde structure of the factor matrix, a spatial smoothing tensor decomposition scheme is introduced to estimate the UAVs’ parameters. To further reduce the computational complexity, we design a reduced-dimensional (RD) angle of arrival (AoA) estimation algorithm based on generalized Rayleigh quotient (GRQ). In Stage II, the positions and true velocities of the UAVs are determined through the data fusion across the multiple base stations (BSs). Specifically, we first develop a false removing minimum spanning tree (MST)-based data association method to accurately match the BSs’ parameter estimations to the same UAV. Then, a Pareto optimality method and a residual weighting scheme are developed to facilitate the position and velocity estimation, respectively. We further extend our approach to the dual-polarized system. Simulation results validate the effectiveness of the proposed schemes in comparison to conventional techniques.

In this paper, we investigate a double-active-reconfigurable intelligent surface (RIS)-aided downlink wireless communication system, where a multi-antenna base station (BS) serves multiple single-antenna users with both double reflection and single reflection links. Due to the signal amplification capability of active RISs, they can effectively mitigate the multiplicative fading effect. However, this also induces signal bouncing between the two active RISs that cannot be ignored. This phenomenon is termed as the “inter-excitation” effect and is characterized in the received signal by proposing a feedback-type model. Based on the signal model, we formulate a weighted sum rate (WSR) maximization problem by jointly optimizing the beamforming matrix at the BS and the reflecting coefficient matrices at the two active RISs, subject to power constraints at the BS and active RISs, as well as the maximum amplification gain constraints of the active RISs. To solve this non-convex problem, we first transform the problem into a more tractable form using the fractional programming (FP) method. Then, by introducing auxiliary variables, the problem can be converted into an equivalent form that can be solved by using a penalty dual decomposition (PDD) algorithm. Furthermore, the power scaling order of the signal-to-noise ratio (SNR) in double-active-RIS-aided system considering inter-excitation effect is derived. Finally, simulation results indicate that the proposed scheme outperforms benchmark schemes with single active RIS and double passive RISs in terms of achievable rate. Furthermore, the results demonstrate that the proposed scheme can enhance the WSR by 30% compared to scenarios that do not take this effect into account when the maximum amplification gain is 40 dB. Additionally, the proposed scheme is capable of achieving high WSR performance at most locations where double active RISs are deployed between the BS and the users, thereby providing greater flexibility in their deployment.

Over-the-air federated learning (OTA-FL) presents a promising distributed machine learning paradigm that improves the efficiency of local update aggregation by leveraging the superposition property of wireless multiple access channels (MACs). However, it faces significant security and privacy concerns that demand careful consideration. To address these threats associated with OTA-FL, we develop a secure and private over-the-air federated learning (SP-OTA-FL) framework, which can realize the secure and private aggregation for both OTA-FL with unbiased aggregation (UB-OTA-FL) and OTA-FL with biased aggregation (B-OTA-FL). In this framework, a subset of devices participate in training, while another subset functions as jammers, emitting jamming signals to enhance the security and privacy of the OTA-FL process. In particular, we measure the privacy leakage of users’ data using differential privacy (DP) and introduce an innovative application of mean squared error security (MSE-security) to evaluate the security of the OTA-FL system. We conduct convergence analyses for both convex and non-convex loss functions. Building on these analytical results, we separately formulate optimization problems for UB-OTA-FL and B-OTA-FL to enhance the learning performance of SP-OTA-FL by strategically optimizing the scheduling of training participants and jammers. The effectiveness of the proposed schemes is verified through simulations.

This paper addresses the fairness issue within fluid antenna system (FAS)-assisted non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) systems, where a single fixed-antenna base station (BS) transmits superposition-coded signals to two users, each with a single fluid antenna. We define fairness through the minimization of the maximum outage probability for the two users, under total resource constraints for both FAS-assisted NOMA and OMA systems. Specifically, in the FAS-assisted NOMA systems, we study both a special case and the general case, deriving a closed-form solution for the former and applying a bisection search method to find the optimal solution for the latter. Moreover, for the general case, we derive a locally optimal closed-form solution to achieve fairness. In the FAS-assisted OMA systems, to deal with the non-convex optimization problem with coupling of the variables in the objective function, we employ an approximation strategy to facilitate a successive convex approximation (SCA)-based algorithm, achieving locally optimal solutions for both cases. Besides, we address a more general scenario involving interference and channel estimation overheads, deriving exact users’ outage probabilities and employing a combination of bisection, one-dimensional (1D) search, and SCA algorithms to efficiently and effectively solve max-min optimization problems in both NOMA and OMA systems, significantly enhancing system fairness and computational efficiency. Our numerical results demonstrate that the proposed schemes significantly enhance outage performance over conventional OMA and NOMA benchmarks, even in the presence of interference, confirming their effectiveness in realistic scenarios. The performance of our closed-form and SCA algorithm-based solutions in FAS-assisted NOMA and OMA systems closely approaches that of the optimal solutions, further validated by the effective approximation of users’ outage probabilities in simulations.

In this study, we present a hybrid deep reinforcement learning (DRL) algorithm, trained using vehicular federated learning (VFL), specifically tailored for dynamic vehicular networks with historical data. Our approach utilizes VFL-based DRL to refine the caching scheme in these networks, focusing on predicting and storing the most effective content nearby to enhance cache efficiency and reduce content request delays. We propose a modified proximal policy optimization (mPPO) based approach for the DRL-based decision-making for caching management, which combines the advantages of proximal policy optimization (PPO) and double deep Q-network (DDQN). Our study encompasses a vehicular framework that includes a central edge node (CEN), roadside units (RSUs), unmanned aerial vehicles (UAVs), and vehicles equipped with historical data. We tackle the challenges posed by varying vehicle density and mobility, non-uniform RSU coverage, and constrained caching capacity. Through comprehensive simulations, we demonstrate that mPPO outperforms conventional DRL methods like PPO and DDQN, as well as heuristic approaches. These results underscore the efficacy of the VFL-based mPPO in dynamic vehicular networks, confirming its potential for real-world applications.

In this paper, we consider rate splitting multiple access (RSMA) based simultaneous refracting/transmitting and reflecting (STAR)-reconfigurable intelligent surface (RIS) aided downlink wireless network for the data transmission from an access point (AP) to two Internet-of-Things devices (IoDs) over Nakagami fading channel. AP executes the integrated sensing and communication (ISAC) principle to eliminate the issue of undesired interference between a communication system and a target. RIS association with RSMA is used in the system to enhance the quality of signal at a higher sum rate. To evaluate the performance of the proposed system, we analyze the outage probability and ergodic sum rate. Simulation results show the impact of the diversity order of Nakagami parameter, and configurable elements of RIS on the system performance along with the sensing performance of AP. Almost 10% rate enhancement is achieved through RSMA compared with non-orthogonal multiple access (NOMA) technique at 10 dBm transmit power.

This paper explores simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted multi-user downlink communications, focusing on maximizing information secrecy despite channel state information (CSI) errors. Perfect CSI is hard to achieve due to limited radio frequency chains at the STAR-RIS, noise, interference, and non-collaborative eavesdroppers. The study addresses the worst-case robust beamforming design problem to maximize the sum secrecy rate, considering transmit power limits, quality of service requirements, and practical constraints on the STAR-RIS phase shifter array. The S-procedure is used to estimate semi-infinite inequality constraints, followed by alternating optimization with a line search to iteratively update the precoder and phase shift matrix. Simulation results highlight STAR-RIS’s superior secrecy performance over conventional RIS and the algorithm’s efficiency across various scenarios.

This paper proposes a framework for integrating sensing, energy, and communication (ISEAC) in a single system. Specifically, the base station (BS) transmits the same signals to the information receivers (IRs), energy harvesting receivers (ERs), and sensing nodes. We aim to optimize the transmission beamforming to minimize the Cramér-Rao bound (CRB) while guaranteeing the performance of the IRs and ERs. We then solve this optimization problem by employing the semidefinite relaxation (SDR) method and Gaussian randomization algorithm. We provide a suboptimal solution with the closed-form solution for the special case with a single IR and a single ER. Finally, simulation results demonstrate the effectiveness of the proposed optimal beamforming design.

This paper investigates the performance of simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) assisted massive multiple-input multiple-output (MIMO) systems with direct links. We apply the two-timescale scheme to design the base station (BS) beamforming and the phase shifts of the STAR-RIS. Specifically, we derive the closed-form expression of the average achievable rate. Based on the derived rate, we theoretically draw insights from comparing STAR-RIS and conventional RIS under the same condition. Then, we optimize the phase shifts of the STAR-RIS using accelerated gradient ascent algorithm. Finally, numerical results are provided to validate our theoretical insights that STAR-RIS outperforms the conventional RIS under some special cases.

In this paper, we investigate an reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) system. Our objective is to maximize the achievable sum rate of the multi-antenna communication users through the joint active and passive beamforming. Weighted minimum mean-square error (WMMSE) method is used to reformulate the original problem into an equivalent one. Then, we utilize an alternating optimization (AO) approach to separate the optimization variables and decompose this challenging problem into two subproblems. Given reflecting coefficients, a penalty-based algorithm is utilized to deal with the transmit power and the non-convex radar signal-to-noise ratio (SNR) constraints. For the given beamforming matrix of the BS, we apply majorization-minimization (MM) to transform the problem into a quadratic constraint quadratic programming (QCQP) problem, which is ultimately solved using a semidefinite relaxation (SDR)-based algorithm. Simulation results illustrate the advantage of deploying RIS in the considered multi-user MIMO (MU-MIMO) ISAC systems.

Extremely large-scale multiple-input multiple-output (XL-MIMO) is regarded as one of the key technologies for future 6G networks, which can further improve spectral efficiency by deploying far more antennas than conventional massive MIMO systems. However, beam training in multiuser XL-MIMO systems is challenging. To tackle this issue, we propose a graph neural network (GNN)-based beam training scheme for the multiuser XL-MIMO system, in which only the far-field wide beams need to be tested for each user. Specifically, the GNN is utilized to map the beamforming gain information of the far-field wide beams to the optimal near-field beam for each user, where the information of the surrounding users can also be utilized by the GNN to further improve the accuracy of the beam training. Simulation results show that the performance of the proposed scheme can approach that of the exhaustive scheme but has more than a 93 % reduction in the pilot overhead.

Reconfigurable intelligent surfaces (RISs) have emerged as a disruptive technology that can reconfigure wireless communication environments cost-effectively. In order to fully unveil the potential of RIS-aided wireless communications, some existing contributions considered the double cooperative passive RISs to achieve a higher capacity scaling orders. However, due to the multiplicative fading effect, the double cooperative passive RISs performs poorly when deployed far from the BS and user respectively. To address this issue, we investigate double cooperative active RISs which are equipped with amplifiers and can overcome the severe path loss caused by the multiplicative fading. Specifically, we aim to maximize the downlink achievable rate subject to the transmit power constraints of the base station (BS) and the double active RISs. The formulated problem is solved by using an alternating optimization (AO) algorithm based on the majorization-minimization (MM) algorithm and the fractional programming (FP) method. Simulation results demonstrate that much better rate performance can be achieved by adopting active RIS compared to passive RIS in the double RIS-aided systems. Meanwhile, deploying them appropriately far away from the BS and user and more elements assigned to the active RIS near the user will achieve better performance.

We present a novel fast beam training method for fast moving targets in millimeter wave (mmWave) wideband radar systems. True-time-delayers (TTDs) are utilized to generate frequency-dependent radar rainbow beams using one orthogonal frequency-division multiplexing (OFDM) symbol, simultaneously covering targets located in the entire angular space for fast beam training. We first propose a scheme based on a single-antenna radar receiver. It can effectively detect and estimate different parameters of interest of targets, including their angles, distance related delays, and velocity related Doppler frequencies, but faces a Doppler ambiguity challenge. To tackle this limitation, we further introduce a scheme based on a multi-antenna receiver, which provides high-precision estimation performance. Simulation results reveal the effectiveness of the proposed rainbow beam-based training method for detecting and estimating mobile targets.

In this study, we present a hybrid deep reinforcement learning (DRL) algorithm, trained using vehicular federated learning (VFL), specifically tailored for dynamic vehicular networks with historical data. Our approach utilizes VFL-based DRL to refine the caching scheme in these networks, focusing on predicting and storing the most effective content nearby to enhance cache efficiency and reduce content request delays. We propose a modified proximal policy optimization (mPPO)-based approach for the DRL-based decision making for caching management, which combines the advantages of proximal policy optimization (PPO) and double deep Q-network (DDQN). Our study encompasses a vehicular framework that includes a central edge node (CEN), roadside units (RSUs), unmanned aerial vehicles (UAVs), and vehicles equipped with historical data. We tackle the challenges posed by varying vehicle density and mobility, nonuniform RSU coverage, and constrained caching capacity. Through comprehensive simulations, we demonstrate that the mPPO outperforms the conventional DRL methods like PPO and DDQN, as well as heuristic approaches. These results underscore the efficacy of the VFL-based mPPO in dynamic vehicular networks, confirming its potential as a viable solution for real-world applications.

This paper presents a comprehensive framework for jointly analyzing the angle estimation error and designing a three-dimensional (3D) positioning algorithm for an Internet of Things (IoT) millimeter wave (mmWave) positioning system. Initially, the azimuth and elevation angles of arrival (AoAs) at the anchors are estimated by applying the two-dimensional discrete Fourier transform (2D-DFT) algorithm. The angle estimation error is then analyzed in terms of probability density functions (PDF) by utilizing the properties of the 2D-DFT algorithm and employing challenging derivations and linear approximations. The analysis reveals that the resulting angle estimation error is non-Gaussian, distinguishing it from previous studies. Next, the complex expression of the PDF for the AoA estimation error is simplified using the first-order linear approximation of triangle functions. Subsequently, a complex expression for the variance is derived based on the obtained PDF. Specifically, the variance for the azimuth estimation error is integrated separately according to the different non-zero intervals of the obtained PDF. Additionally, the closed-form expressions of the variances are formulated using generalized hypergeometric series. Finally, the two-stage weighted least square (TSWLS) algorithm is employed to estimate the 3D position of the mobile user (MU) using the estimated AoAs and the obtained non-Gaussian variance. Extensive simulation results confirm the non-Gaussian nature of the derived angle estimation error and demonstrate the superiority of the proposed framework.

Federated learning (FL) has been widely investigated in academic and industrial fields to resolve the issue of data isolation in the distributed Internet of Things (IoT) while maintaining privacy. However, challenges persist in ensuring adequate privacy and security during the aggregation process. In this article, we investigate device scheduling strategies that ensure the security and privacy of wireless FL. Specifically, we measure the privacy leakage of user data using differential privacy (DP) and assess the security level of the system through the mean-square error security (MSE-security). We commence by deriving the analytical results that reveal the impact of the device scheduling on privacy and security protection, as well as on the learning process. Drawing from these analytical findings, we propose three scheduling policies that can achieve secure aggregation of wireless FL under different cases of channel noise. In particular, we formulate an integer nonlinear fractional programming problem to improve the learning performance while guaranteeing privacy and security of wireless FL. We provide an insightful solution in the closed form to the optimization problem when the model has a high dimension. For the general case, we propose a secure and private aggregation (SPA) algorithm based on the branch-and-bound (BnB) method, which can obtain the optimal solution with low complexity. The effectiveness of the proposed schemes for device selection is validated through simulations.

This paper proposes a novel localization algorithm using the reconfigurable intelligent surface (RIS) received signal, i.e., RIS information. Compared with BS received signal, i.e., BS information, RIS information offers higher dimension and richer feature set, thereby providing an enhanced capacity to distinguish positions of the mobile users (MUs). Additionally, we address a practical scenario where RIS contains some unknown (number and places) faulty elements that cannot receive signals. Initially, we employ transfer learning to design a two-phase transfer learning (TPTL) algorithm, designed for accurate detection of faulty elements. Then our objective is to regain the information lost from the faulty elements and reconstruct the complete high-dimensional RIS information for localization. To this end, we propose a transfer-enhanced dual-stage (TEDS) algorithm. In Stage I, we integrate the CNN and variational autoencoder (VAE) to obtain the RIS information, which in Stage II, is input to the transferred DenseNet 121 to estimate the location of the MU. To gain more insight, we propose an alternative algorithm named transfer-enhanced direct fingerprint (TEDF) algorithm which only requires the BS information. The comparison between TEDS and TEDF reveals the effectiveness of faulty element detection and the benefits of utilizing the high-dimensional RIS information for localization. Besides, our empirical results demonstrate that the performance of the localization algorithm is dominated by the high-dimensional RIS information and is robust to unoptimized phase shifts and signal-to-noise ratio (SNR).

Extremely large-scale multiple-input multiple-output (XL-MIMO) is capable of supporting extremely high system capacities with large numbers of users. In this work, we build a framework for the analysis and low-complexity design of XL-MIMO in the near field with spatial non-stationarities. Specifically, we first analyze the theoretical performance of discrete-aperture XL-MIMO using an electromagnetic (EM) channel model based on the near-field spherical wavefront. We analytically reveal the impact of the discrete aperture and polarization mismatch on the received power. We also complement the classical Fraunhofer distance based on the considered EM channel model. Our analytical results indicate that a limited part of the XL-array receives the majority of the signal power in the near field, which leads to a notion of visibility region (VR) of a user. Thus, we propose a VR detection algorithm and leverage the acquired VR information to devise a low-complexity symbol detection scheme. Furthermore, we propose a graph theory-based user partition algorithm, relying on the VR overlap ratio between different users. Partial zero-forcing (PZF) is utilized to eliminate only the interference from users allocated to the same group, which further reduces computational complexity in matrix inversion. Numerical results confirm the correctness of the analytical results and the effectiveness of the proposed algorithms. It reveals that our algorithms approach the performance of conventional whole array (WA)-based designs but with much lower complexity.

This study proposes a two-timescale transmission scheme for extremely large-scale reconfigurable intelligent surface aided (XL-RIS-aided) massive multi-input multi-output (MIMO) systems in the presence of visibility regions (VRs). The beamforming of base stations (BSs) is designed based on rapidly changing instantaneous channel state information (CSI), while the phase shifts of RIS are configured based on slowly varying statistical CSI. Specifically, we first formulate a system model with spatially correlated Rician fading channels and introduce the concept of VRs. Then, we derive a closed-form approximate expression for the achievable rate and analyze the impact of VRs on system performance and computational complexity. Then, we solve the problem of maximizing the minimum user rate by optimizing the phase shifts of RIS through an algorithm based on accelerated gradient ascent. Finally, we present numerical results to validate the performance of the considered system from different aspects and reveal the low system complexity of deploying XL-RIS in massive MIMO systems with the help of VRs.

Abstract Near-field technology is increasingly recognized due to its transformative potential in communication systems, establishing it as a critical enabler for sixth-generation (6G) telecommunication development. This paper presents a comprehensive survey of recent advancements in near-field technology research. First, we explore the near-field propagation fundamentals by detailing definitions, transmission characteristics, and performance analysis. Next, we investigate various near-field channel models—deterministic, stochastic, and electromagnetic information theory based models, and review the latest progress in near-field channel testing, highlighting practical performance and limitations. With evolving channel models, traditional mechanisms such as channel estimation, beamtraining, and codebook design require redesign and optimization to align with near-field propagation characteristics. We then introduce innovative beam designs enabled by near-field technologies, focusing on non-diffractive beams (such as Bessel and Airy) and orbital angular momentum (OAM) beams, addressing both hardware architectures and signal processing frameworks, showcasing their revolutionary potential in near-field communication systems. Additionally, we highlight progress in both engineering and standardization, covering the primary 6G spectrum allocation, enabling technologies for near-field propagation, and network deployment strategies. Finally, we conclude by identifying promising future research directions for near-field technology development that could significantly impact system design. This comprehensive review provides a detailed understanding of the current state and potential of near-field technologies.

This paper investigates the performance of simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted massive multiple-input multiple-output (MIMO) systems with Rician fading channels and channel estimation errors. We adopt the two-timescale scheme to design the systems, namely, applying the instantaneous channel state information (CSI) to design the base station (BS) beamforming and leveraging the statistical CSI to design the phase shifts of the STAR-RIS. Specifically, we estimate the overall channels based on the linear minimum mean-squared error (LMMSE) estimator and derive the closed-form expression of the average achievable rate. Based on the derived rate, we analyze the power scaling laws in which the transmit power is respectively reduced inversely proportional to the number of BS antennas and STAR-RIS elements. Besides, we draw insights from the comparison between STAR-RIS and conventional RIS under the same condition and the power scaling laws of STAR-RIS and optimize the phase shifts of the STAR-RIS to maximize the sum rate using an accelerated gradient ascent-based algorithm. Finally, numerical results are provided to validate our theoretical insights. In particular, we also compare the two-timescale scheme with the instantaneous CSI scheme in the simulation. We show that STAR-RIS outperforms conventional RIS, and the two-timescale-based scheme outperforms the instantaneous CSI-based scheme. Furthermore, we draw insight into this phenomenon.

This paper considers an intelligent reflecting surface (IRS) aided secure communication with direct communication links where a legitimate receiver (Bob) served by a base station (BS) is overheard by multiple eavesdroppers (Eves), meanwhile the artificial noise (AN) is incorporated to confuse Eves. Since Eves are not legitimate users, their channels cannot be estimated perfectly. We investigate two scenarios with partial channel state information (CSI) error of only cascaded BS-IRS-Eve channel and full CSI errors of both cascaded BS-IRS-Eve channel and direct BS-Eve channel under the statistical CSI error model. To ensure the security performance under CSI errors, the transmit beamforming, AN spatial distribution at the BS, and phase shifts at IRS are jointly optimized to minimize the transmit power constrained by the minimum data rate requirement of Bob and the outage probability of maximum data rate limitation of Eves. In contrast to existing works, the direct link considered in our work makes the optimization of phase shifts at IRS much more challenging, thus we propose a series of novel and artful mathematical manipulations to tackle this issue. Moreover, the proposed algorithm can be applied for both uncorrelated and correlated CSI errors. Simulations confirm the superiority of our proposed algorithm.

Next-generation wireless applications are expected to enable extended ultra-reliable low-latency communication (URLLC) to support high data rates along with ultra-reliability and low-latency features beyond the capabilities of existing core services. There is a need to transition from conventional architectures to more efficient and robust multiple-access schemes to meet these consolidated requirements in resource-constrained systems. This study explores the utilization of simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) in non-orthogonal multiple access (NOMA) systems to enable spectrally efficient URLLC, even under the imperfect channel state information. In particular, we focus on maximizing spectral efficiency by jointly designing robust beamforming at the base station and STAR-RIS subject to given URLLC requirements. Due to the non-convexity of the formulated problem, we propose an alternating optimization framework that obtains sub-optimal solutions to the problems of beamforming design at the BS and STAR-RIS, respectively by exploiting$\mathcal {S}-$procedure and successive convex approximation. Simulation results confirm that the STAR-RIS-NOMA system can significantly boost the spectral efficiency by 10-15% compared to conventional reflecting-only RIS while guaranteeing the strict URLLC requirements. Specifically, among all the possible modes of STAR-RIS, the time-splitting mode provides better spectral efficiency than other modes owing to its better interference management.

This paper investigates a digital twin (DT) and reconfigurable intelligent surface (RIS)-aided mobile edge computing (MEC) system under given constraints on ultra-reliable low latency communication (URLLC). In particular, we focus on the problem of total end-to-end (E2E) latency minimization for the considered system under the joint optimization of beamforming design at the RIS, power, bandwidth allocation, processing rates, and task offloading parameters using DT architecture. To tackle the formulated non-convex optimization problem, we first model it as a Markov decision process (MDP). Later, we adopt deep deterministic policy gradient (DDPG) based deep reinforcement learning (DRL) algorithm to solve it effectively. We have compared the DDPG results with proximal policy optimization (PPO), modified PPO (M-PPO), and conventional alternating optimization (AO) algorithms. Simulation results depict that the proposed DT-enabled resource allocation scheme for the RIS-empowered MEC network using DDPG algorithm achieves up to 60% lower transmission delay and 20% lower energy consumption compared to the scheme without an RIS. This confirms the practical advantages of leveraging RIS technology in MEC systems. Results demonstrate that DDPG outperforms M-PPO and PPO in terms of higher reward value and better learning efficiency, while M-PPO and PPO exhibit lower execution time than DDPG and AO due to their advanced policy optimization techniques. Thus, the results validate the effectiveness of the DRL solutions over AO for dynamic resource allocation w.r.t. reduced execution time.

This paper develops an optimal design for device scheduling, alignment coefficient, and aggregation rounds within a differentially private over-the-air federated averaging (DP-OTA-FedAvg) system considering a constrained sum power budget. In DP-OTA-FedAvg, gradients are aligned using an alignment coefficient and then aggregated over the air, utilizing channel noise to ensure participant privacy. This study highlights two critical tradeoffs in aligned over-the-air federated learning (OTA-FL) systems with limited power and privacy budgets. Firstly, it reveals the tradeoff between the number of scheduled devices and the alignment coefficient. Secondly, it investigates the balance between aggregation distortion and local training error while adhering to the sum power constraint. Specifically, we measure privacy using differential privacy (DP) and perform convergence analyses for both convex and non-convex loss functions. These analyses provide insights into how device scheduling, the alignment coefficient, and the number of global aggregations affect both privacy preservation and the learning process. Building on these analytical results, we formulate an optimization problem aimed at minimizing the optimality gap of DP-OTA-FedAvg under power and privacy constraints. By specifying the number of aggregation rounds, we derive a closed-form expression describing the relationship between the alignment coefficient and the number of scheduled devices. We then tackle the problem through iterative optimization of scheduling and aggregation rounds. The effectiveness of the proposed policies is verified through simulations, and the performance advantage is particularly pronounced in scenarios where devices have poor channel conditions and limited sum-power budgets.

This paper considers an active reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) system. We aim to maximize radar signal-to-interference-plus-noise-ratio (SINR) by jointly optimizing the beamforming matrix at a dual-function radar-communication (DFRC) base station (BS) and the reflecting coefficients at an active RIS subject to the quality of service (QoS) constraints of communication user equipments (UEs) and the transmit power constraints of active RIS and DFRC BS. To tackle the optimization problem, the majorization-minimization (MM) algorithm is applied to address the nonconvex radar SINR objective function, and the resulting quartic problem is solved by developing an semidefinite relaxation (SDR)-based approach. Moreover, we derive the scaling order of the radar SINR with a large number of reflecting elements. Next, the transmit power allocation problem and the deployment strategy of the active RIS are studied with a moderate number of reflecting elements. Finally, we validate the potential of the active RIS in ISAC systems compared to passive RIS. Additionally, we deliberate on several open problems that remain for future research.

This paper investigates the performance of a two-timescale transmission design for uplink reconfigurable intelligent surface (RIS)-aided cell-free massive multiple-input multiple-output (CF-mMIMO) systems. We consider the Rician channel model and design the passive beamforming of RISs based on the long-time statistical channel state information (CSI), while the central processing unit (CPU) utilizes the maximum ratio combining (MRC) technology to perform fully centralized processing based on the instantaneous overall channel, which is the superposition of the direct and RIS-reflected channels. Firstly, we derive the closed-form approximate expression of the uplink achievable rate for arbitrary numbers of access point (AP) antennas and RIS reflecting elements, which can be used to obtain energy efficiency through the proposed total power model. Relying on the derived expressions, we theoretically analyze the impact of important system parameters on the rate and draw explicit insights into the benefits of RISs. Then, based on the rate expression under statistical CSI, we optimize the phase shifts of RISs by using the genetic algorithm (GA) to maximize the sum rate and minimum rate of users, respectively. Finally, the numerical results demonstrate the correctness of our expressions and the benefits of deploying large-size RISs into cell-free mMIMO systems. Also, we investigate the optimality and convergence behaviors of the GA to verify its effectiveness. To give a more beneficial analysis, we present numerical results to show the high energy efficiency of the system with the help of RISs. Besides, our results have revealed the benefits of distributed deployment of APs and RISs in the RIS-aided mMIMO system with cell-free networks.

Intelligent reflecting surface (IRS) is a promising technology that provides high throughput in future communication systems and is compatible with various communication techniques, such as non-orthogonal multiple-access (NOMA). This paper studies the downlink transmission of IRS-assisted NOMA communication, considering the practical case of imperfect channel state information (CSI). Aiming to maximize the system sum rate, a robust IRS-aided NOMA design is proposed to jointly find the optimal beamforming vector for the access point and the passive reflection matrix for the IRS. This robust design is realised using the penalty dual decomposition (PDD) scheme, and it is shown that the results have a close performance to their upper bound obtained from the corresponding perfect CSI scenario. The presented method is compatible with both continuous and discrete phase shift elements of the IRS. Our findings show that the proposed algorithms, for both continuous and discrete IRS, have low computational complexity compared to other schemes in the literature. Furthermore, we conduct a performance comparison between the IRS-aided NOMA and the IRS-aided orthogonal multiple access (OMA). This comparison shows that robust beamforming techniques are crucial for the system to reap the advantages of IRS-aided NOMA communication in the presence of CSI uncertainty.

In this paper, to tackle the blockage issue in massive multiple-input-multiple-output (mMIMO) systems, a reconfigurable intelligent surface (RIS) is seamlessly deployed to support devices with ultra-reliable and low-latency communications (URLLC). The transmission power of the base station and the phase shifts of the RIS are jointly devised to maximize the weighted sum rate while considering the spatially correlation and channel estimation errors. Firstly, the relationship between the channel estimation error and spatially correlated RIS’s elements is revealed by using the linear minimum mean square error. Secondly, based on the maximum-ratio transmission precoding, a tight lower bound of the rate under short packet transmission is derived. Finally, the NP-hard problem is decomposed into two optimization problems, where the transmission power is obtained by geometric programming and phase shifts are designed by using gradient ascent method. Besides, we have rigorously proved that the proposed algorithm can rapidly converge to a sub-optimal solution with low complexity. Simulation results confirm the tightness between the analytic results and Monte Carlo simulations. Furthermore, the two-timescale scheme provides a practical solution for the short packet transmission.

In this paper, we consider an active reconfigurable intelligent surface (RIS) to assist the multiuser downlink transmission in the presence of practical hardware impairments (HWIs), including the HWIs at the transceivers and the phase noise at the active RIS. The active RIS is deployed to amplify the incident signals to alleviate the multiplicative fading effect, which is a limitation in the conventional passive RIS-aided wireless systems. We aim to maximize the sum rate through jointly designing the transmit beamforming at the base station (BS), the amplification factors and the phase shifts at the active RIS. To tackle this challenging optimization problem effectively, we decouple it into two tractable subproblems. Subsequently, each subproblem is transformed into a second order cone programming problem. The block coordinate descent framework is applied to tackle them, where the transmit beamforming and the reflection coefficients are alternately designed. In addition, another efficient algorithm is presented to reduce the computational complexity. Specifically, by exploiting the majorization-minimization approach, each subproblem is reformulated into a tractable surrogate problem, whose closed-form solutions are obtained by Lagrange dual decomposition approach and element-wise alternating sequential optimization method. Simulation results validate the effectiveness of our developed algorithms, and reveal that the HWIs significantly limit the system performance of active RIS-empowered wireless communications. Furthermore, the active RIS noticeably boosts the sum rate under the same total power budget, compared with the passive RIS.

With the increasing security concerns of the satellite network due to the broadcasting nature and the inherent openness of satellite-terrestrial communications, the satellite spectrum and terrestrial node resource trading based cooperation in cognitive satellite terrestrial networks (CSTNs) has gained a lot attention. However, the existing literature has not well considered the fairness issue in resource trading, which may cause cooperation failure between the satellite and terrestrial networks when their own benefits are impaired. To tackle this issue, in this paper we propose a two-layer hierarchical game framework for a multi-terrestrial base stations (BSs) CSTN scenario to guarantee the fairness of resource trading between the satellite and terrestrial networks and thus achieve a win-win situation for both networks. Specifically, a coalition formation game is adopted to study the cooperative behaviors among the terrestrial BSs. Herein, we propose a distributed merge-and-split based coalition formation algorithm to determine the coalition structure, of which the stability, convergence, and complexity are theoretically investigated. Moreover, a Stackelberg game is introduced to model the competition between the satellite and terrestrial BSs, where the satellite acts as the leader and the terrestrial BSs act as the followers. The Stackelberg equilibrium (SE) for the Stackelberg game is derived based on the backward induction method. We then design a distributed algorithm to obtain the coalition structure and SE for the proposed two-layer hierarchical game framework. Finally, simulations are presented to validate our theoretical results.

As a revolutionary paradigm for green communication architecture for next-generation, reconfigurable intelligent surfaces (RISs) has been considered for simultaneous wireless information and power transfer (SWIPT). Nevertheless, the performance gain achieved by the conventional passive RISs is limited due to the multiplicative fading effect. In this paper, we investigate an unconventional framework of active reconfigurable intelligent surface (ARIS) aided multi-user (MU) multi-input multi-output (MIMO) system to captivate better performance for the SWIPT system. Particularly, we focus on the problem of power minimization via joint beamforming design at the base station (BS) and the ARIS for the considered SWIPT system under statistical channel estimation error (CEE) while guaranteeing the minimum rate requirement and the minimum energy-harvested constraints for information and energy receivers, respectively. Owing to the non-convex and NP-hard nature of the formulated problem, we first utilize a minimum mean square error (MMSE) approach to transform the problem into its simplified form, and later utilize an alternating optimization framework which solves the problems of beamforming design at the BS and the ARIS independently in an iterative manner using general approximations. Simulation results confirm that the ARIS can significantly reduce the required transmission power by 50-60% when compared to passive RIS while satisfying given QoS constraints for SWIPT system under the CEE model.

We propose new pilot transmission protocols for acquiring channel state information (CSI) of individual reconfigurable intelligent surface (RIS) assisted channels. Our approach addresses the challenge of individual CSI acquisition when the RIS lacks sensing and signal processing capabilities. We use monostatic and bistatic full-duplex base stations (BSs) and exploit the reciprocity of the uplink and downlink channels to design channel estimation algorithms based on both unstructured and geometric channel models. Specifically, for unstructured channel models, we develop two different channel estimation algorithms that provide high accuracy and low pilot overhead, respectively, depending on the type of full-duplex BS used. Moreover, a unified estimation framework is proposed to determine the CSI based on geometric channel models for both types of full-duplex BSs. For the angle estimation required as part of the proposed framework, we further develop a high-precision algorithm based on atomic norm minimization (ANM) and a low-complexity algorithm based on orthogonal matching pursuit (OMP). Simulation results reveal that the proposed algorithms are superior to existing methods in terms of estimation accuracy, complexity, and pilot overhead.

Active reconfigurable intelligent surfaces (RISs) have recently been proposed to compensate for the severe multiplicative fading effect of conventional passive RIS-aided systems. Each reflecting element of active RISs is assisted by an amplifier such that the incident signal can be reflected and amplified instead of only being reflected as in passive RIS-aided systems. This work addresses the practical challenge that, on the one hand, in active RIS-aided systems the perfect individual channel state information (CSI) of the RIS-aided channels cannot be acquired due to the lack of signal processing power at the active RISs, but, on the other hand, this CSI is required to calculate the expected system data rate and RIS transmit power needed for transceiver design. To address this issue, we first derive closed-form expressions for the average achievable rate and the average RIS transmit power based on partial CSI of the RIS-aided channels. Then, we formulate an average achievable rate maximization problem for jointly optimizing the active beamforming at both the base station (BS) and the RIS. This problem is then tackled using the majorization–minimization (MM) algorithm framework, and, in each iteration low-complexity solutions for the BS and RIS beamforming are found based on the Karush-Kuhn-Tucker (KKT) conditions. To ensure the quality of service (QoS) of each user, we further formulate a rate outage constrained beamforming problem, which is solved using the Bernstein-Type inequality (BTI) and semidefinite relaxation (SDR) techniques. Numerical results show that the proposed algorithms can efficiently overcome the challenges imposed by imperfect CSI in active RIS-aided wireless systems.

In this paper, we propose a novel approach for optimal resource management in ultra-reliable low-latency communication (URLLC)-enabled Internet of Vehicles (IoV) networks. The framework includes mobile edge computing (MEC) servers integrated into roadside units (RSUs), unmanned aerial vehicles (UAVs), and base stations (BSs) for hybrid vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. We utilize asynchronous federated learning (AFL) approach to enhance the accuracy of the global model by considering the mobility characteristics of vehicles. The problem of optimal resource allocation is formulated to achieve the best allocation of frequency, computation, and caching resources while complying with the delay restrictions. To solve the non-convex problem, a multi-agent actor-critic type deep reinforcement learning algorithm called D-MAAC algorithm is introduced. Extensive simulations show the effectiveness of the proposed framework and algorithms compared to existing schemes.

Next-generation wireless applications are expected to enable extended ultra-reliable low latency communication (URLLC) to support high data rates along with ultra-high reliability and low end-to-end latency features beyond the capabilities of existing core services. This paper investigates simultaneous transmission and reflection re-configurable intelligent surface (STARRIS) aided non-orthogonal multiple access (NOMA) systems to enable spectral-efficient short-packet communication under imperfect channel state information. In particular, we focus on the problem of spectral-efficiency maximization via joint beamforming design at the base station and STAR-RIS subject to given URLLC requirements. Owing to the non-convexity of the formulated problem, we propose an alternating optimization framework that obtains sub-optimal solutions to the robust beamforming design, respectively by exploiting$S$-procedure and successive convex approximation. Simulation results confirm that the STAR-RIS-NOMA system can significantly boost the spectral efficiency by 10-15% compared to conventional reflecting-only RIS while guaranteeing the strict URLLC requirements.

In this paper, the reconfigurable intelligent surface (RIS)-aided massive multiple-input-multiple-output (mMIMO) system with ultra-reliability and low latency communications (URLLC) is investigated. Specifically, the spatial correlation and imperfect channel estate information (CSI) are considered, where the phase shifts of the RIS and the transmission power of the base station (BS) are jointly optimized to maximize the weighted sum rate. Firstly, the aggregated channel is estimated relying on the linear minimum mean square error (LMMSE) method, and the normalized mean square error (NMSE) is analyzed. Secondly, the lower bound for the achievable data rate is derived for maximum-ratio transmission (MRT). Finally, the non-convex problem is separated into two optimization problems. Then, based on the statistical CSI, geometric programming and gradient descent are adopted to optimize the transmission power of the BS and the phase shifts of the RIS, respectively. Simulation results confirm the accuracy of the analytic results and the superiority of our proposed algorithm.

In this paper, we consider Simultaneous Transmission and Reflection (STAR) Reconfigurable Intelligent Surface (S-RIS) and passive RIS (P-RIS) assisted integrated sensing and communication system (ISAC), where S-RIS is enabled to broadcast communication signal, and P-RIS assists sensing functionalities. In particular, we jointly optimize the beamforming vector at the multi-antenna ISAC transmitter, and phase shift vector to maximize the weighted sum-rate (WSR) at the communication users while taking care of the maximum power limit at ISAC transmitter while ensuring the performance of sensing model to detect targets in its vicinity and limitations of phase and amplitude of S-RIS elements. To address the non-convexity of the above problem, we propose a low-complexity alternating optimization (AO) algorithm. Furthermore, we provide a comprehensive simulation-based graphical results to verify the viability of the proposed framework with its P-RIS assisted counterpart. Eventually, exhaustive simulation results are demonstrated to present the impact of RIS elements and the number of antennas at the ISAC transmitter. Accordingly, we illustrate the impact of S-RIS and the number of targets to highlight the trade-off between sensing and communication.

In this paper, we explore a novel approach of using a simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) to aid a multi-user (MU) multi-input multi-output (MIMO) system for simultaneous wireless information and power transfer (SWIPT) in the presence of statistical channel estimation errors (CEE). Our focus is on minimizing the power required for the SWIPT system through joint beamforming design at both the base station (BS) and STAR-RIS, while ensuring that the minimum rate and minimum energy harvesting requirements are met for information and energy receivers, respectively. Due to the non-convex and NP-hard nature of the problem, we utilize a minimum mean square error (MMSE) approach to simplify the problem and then use an alternating optimization framework to solve the beamforming design problems at the BS and STAR-RIS iteratively using general approximations. Simulation results show that the proposed algorithm provides a significant beamforming gain for STAR-RIS-aided SWIPT system over conventional RIS system while satisfying the given quality of service (QoS) constraints for SWIPT systems under the CEE.

In this work, we propose low-complexity designs for XL-MIMO in the near-field with spatial non-stationarities. We first introduce a notion of visibility region (VR) and propose a VR detection algorithm. Then, we exploit the acquired VR information to design a low-complexity detection scheme for XL-MIMO systems. To further reduce the complexity, we propose a graph theory-based user partition algorithm, relying on the VR overlap ratio between different users. Then, partial zero-forcing (PZF) is utilized to eliminate only the interference from users allocated to the same group, which further reduces computational complexity in matrix inversion. Numerical results confirm the effectiveness of the proposed algorithms which approach the performance of conventional whole array (WA)-based designs but with much lower complexity.

The unimodular waveform design for simultaneous sensing and communication plays an important role in the integrated sensing and communication (ISAC) systems. The existing studies mainly include the tradeoff waveform design without Reconfigurable Intelligent Surface (RIS); or the RIS aided-waveform design with optimal performance in a certain aspect, which usually degrade the comprehensive performance. To address these issues, the comprehensive waveform design with RIS is proposed, in which the waveform and RIS are coupled. The existing decoupled methods are mainly Alternating Optimization (AO), which are computationally unaffordable. To solve the problem efficiently, the Parallel Product Complex Circle Manifold (P2C2M) framework is devised using the natural constant mod-ulus characteristic of both the waveform and RIS. Concretely, the problem is converted to the Unconstrained Coupling Quartic Problem (UCQP) over the P2C2M. Based on the P2C2M, the Parallel Conjugate Gradient algorithm is derived to optimize the waveform and RIS in parallel. Compared with the existing methods, the proposed method achieves better comprehensive performance with less computational cost.

We propose new pilot transmission protocols for acquiring channel state information (CSI) of individual reconfig-urable intelligent surface (RIS) assisted channels. Our approach addresses the challenge of individual CSI acquisition when the RIS lacks sensing and signal processing capabilities. We use monostatic and bistatic full-duplex (FD) base stations (BSs) and exploit the reciprocity of the uplink and downlink channels to design channel estimation algorithms. Specifically, a unified estimation framework is proposed to estimate the CSI based on geometric channel models for both types of FD BSs. To handle the angle estimation required as part of the proposed framework, we further investigate a high-accuracy algorithm based on atomic norm minimization (ANM) and a low-complexity algorithm based on orthogonal matching pursuit (OMP). Simulation results reveal that the proposed ANM based estimation scheme for bistatic BSs outperforms that for monostatic BSs, since the former requires a identical pilot overhead and achieves a similar estimation accuracy while having a much simpler hardware implementation.

In this paper, an efficient two-phase channel estimation scheme with reduced pilot overhead and error propagation is proposed for a uniform planar array (UPA)-type reconfigurable intelligent surface (RIS)-aided multi-user (MU) millimeter wave (mmWave) system. In Phase I, based on the carefully designed RIS phase shift matrix, all users jointly transmit the pilot signals to estimate the correlation factors between different propagation paths of the common RIS-base station (BS) channel, which facilitates a significant MU diversity gain. Then, in Phase II, with the constructed ambiguous RIS-BS channel composed of the correlation factors obtained in the previous phase, each user independently sends a few pilots to estimate their own ambiguous user-RIS channel so as to obtain the entire cascaded channel. Simulation results validate that the proposed algorithm outperforms the existing algorithms in terms of both pilot overhead and estimation accuracy, and that its estimation performance improves as the number of users increases.

In this paper, we propose a device scheduling scheme for differentially private over-the-air federated learning (DP-OTA-FL) systems, referred to as S-DPOTAFL, where the privacy of the participants is guaranteed by channel noise. In S-DPOTAFL, the gradients are aligned by the alignment coefficient and aggregated via over-the-air computation (AirComp). The scheme schedules the devices with better channel conditions in the training to avoid the problem that the alignment coefficient is limited by the device with the worst channel condition in the system. We conduct the privacy and convergence analysis to theo-retically demonstrate the impact of device scheduling on privacy protection and learning performance. To improve the learning accuracy, we formulate an optimization problem with the goal to minimize the training loss subjecting to privacy and transmit power constraints. Furthermore, we present the condition that the S-DPOTAFL performs better than the DP-OTA-FL without considering device scheduling (NoS-DPOTAFL). The effectiveness of the S-DPOTAFL is validated through simulations.

To combat the half-space coverage and enhance the flexibility of the reconfigurable intelligent surface (RIS) technology, a simultaneously transmitting and reflecting RIS (STAR-RIS) is applied in the radar-communication co-existence (RCC) system, where the signal through STAR-RIS is transmitted to opposite spaces, and STAR-RIS is utilized to handle the interference from the base station (BS) to the radar. A radar detection probability maximization problem by optimizing the transmit beamforming vector of the BS and the transmission-and reflection-coefficient matrices of the STAR-RIS is formulated, subject to the power constraint of the BS and the communication rate constraints of users. The problem is challenging to solve due to the highly coupled variables. We convert it into two sub-problems and propose an efficient alternating optimization (AO) algorithm to solve this non-convex problem. The simulation results validate the convergence of the proposed algorithm and the performance advantages of using STAR-RIS over conventional RIS.

Abstract This paper focuses on a reconfigurable intelligent surface (RIS)‐aided full‐duplex multi‐user massive multiple‐input multiple‐output system with transceiver hardware impairments (THWIs). Different from the existing works, the two‐timescale design scheme is considered. The phase shift at the RIS is designed only based on the statistical channel state information. Firstly, the closed‐form expression of the uplink and downlink achievable rate is derived. Then, the power scaling laws are revealed. Finally, the impact of THWIs on the system performance is analysed and genetic algorithm to maximize the achievable rate is used.

Abstract This paper studies the beamforming design for an active reconfigurable intelligent surface (RIS) aided non‐orthogonal multiple access (NOMA) network. It aims to minimize the transmission power while satisfying the requirement of the quality of service (QoS). To solve this problem, an iterative algorithm based on approximate transformations and penalty functions is proposed. Finally, simulation results illustrate that an active RIS‐aided NOMA system can achieve significant performance gains over the passive RIS‐aided NOMA network.

Unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) systems offer significant advantages in coverage and deployment flexibility, but suffer from endurance limitations due to the limited onboard energy. This article proposes to improve the energy efficiency of UAV-enabled WPT systems with multiple ground sensors by utilizing reconfigurable intelligent surface (RIS). Specifically, the total energy consumption of the UAV is minimized, while meeting the energy requirement of each sensor. First, we consider a fly-hover-broadcast (FHB) protocol, in which the UAV radiates radio-frequency (RF) signals only at several hovering locations. The energy minimization problem is formulated to jointly optimize the UAV’s trajectory, hovering time, and the RIS’s reflection coefficients. To solve this complex nonconvex problem, we propose an efficient algorithm. Specifically, the successive convex approximation (SCA) framework is adopted to jointly optimize the UAV’s trajectory and hovering time, in which a minorization–maximization (MM) algorithm that maximizes the minimum charged energy of all sensors is provided to update the reflection coefficients. Then, we investigate the general scenario in which the RF signals are radiated during the flight, aiming to minimize the total energy consumption of the UAV by jointly optimizing the UAV’s trajectory, flight time, and the RIS’s reflection coefficients. By applying the path discretization (PD) protocol, the optimization problem is formulated with a finite number of variables. A high-quality solution for this more challenging problem is obtained. Finally, our simulation results demonstrate the effectiveness of the proposed algorithm and the benefits of RIS in energy saving.

Reconfigurable intelligent surface (RIS) has recently been regarded as a potential technique to enhance the performance of wireless communication systems by creating additional communication links. However, it is almost impossible to get the perfect channel state information (CSI) from the base station (BS) to the Internet of Things Devices (IoTDs) and the RIS-related channels. Furthermore, residual transceiver hardware impairments inevitably affect the performance of wireless communication systems. Hence, we study the robust design for an RIS-aided wireless communication system based on the imperfect CSI and hardware impairments. Minimizing the power consumption of BS is formulated by ensuring the minimum signal-to-interference-plus-noise ratio (SINR) demands of the IoTDs and the unit-modulus constraints of the RIS. Specifically, after approximating the nonconvex constraints by using the S-procedure and the successive convex approximation (SCA) methods, we adopt the block coordinate descent (BCD) technique to iteratively optimize one set of variables while keeping the other variables fixed in various channel uncertainty scenarios. Simulation results demonstrate that the influence of transceiver hardware impairments can be effectively decreased by deploying RIS even with channel uncertainty, which is more advantageous than increasing the number of BS’s antennas.

AS ONE of the new communication scenarios in 5th-generation (5G) mobile communication systems, ultra-reliable and low-latency communications (URLLC) have stringent requirements on latency (around 1 ms) and reliability (up to 99.99999%). Nevertheless, existing 5G URLLC alone cannot fulfill all the Key Performance Indicators (KPIs) in emerging mission-critical applications like industrial automation, intelligent transportation, telemedicine, Tactile Internet, and Virtual/Augmented Reality (VR/AR). The 6th generation (6G) communication systems need to meet additional requirements on some of the following KPIs in combination with URLLC: high spectrum efficiency (SE)/throughput/energy efficiency (EE)/network availability/security as well as low Age of Information (AoI)/jitter/round-trip delay. These new requirements pose unprecedented challenges in terms of design methodologies and enabling technologies in 6G. To fill the gap between 5G URLLC and the diverse KPI requirements of the neXt generation URLLC (xURLLC), novel methodologies and innovative technologies are much needed.

Extremely large-scale reconfigurable intelligent surface (XL-RIS) has recently been proposed and is recognized as a promising technology that can further enhance the capacity of communication systems and compensate for severe path loss. However, the pilot overhead of beam training in XL-RIS-assisted wireless communication systems is enormous because the near-field channel model needs to be taken into account, and the number of candidate codewords in the codebook increases dramatically. To tackle this problem, we propose two deep learning-based near-field beam training schemes in XL-RIS-assisted communication systems, where deep residual networks are employed to determine the optimal near-field RIS codeword. Specifically, we first propose a far-field beam-based beam training (FBT) scheme in which the received signals of all far-field RIS codewords are fed into the neural network to estimate the optimal near-field RIS codeword. In order to further reduce the pilot overhead, a partial near-field beam-based beam training (PNBT) scheme is proposed, where only the received signals corresponding to the partial near-field XL-RIS codewords are input to the neural network. Moreover, we further propose an improved PNBT scheme to enhance the performance of beam training by fully exploring the neural network’s output. Finally, simulation results show that the proposed schemes outperform the existing beam training schemes and can reduce the beam sweeping overhead by approximately 95%.

Smart factories need to support the simultaneous communication of multiple industrial Internet-of-Things (IIoT) devices with ultra-reliability and low-latency communication (URLLC). Meanwhile, short packet transmission for IIoT applications incurs performance loss compared to traditional long packet transmission for human-to-human communications. On the other hand, cell-free massive multiple-input and multiple-output (CF mMIMO) technology can provide uniform services for all devices by deploying distributed access points (APs). In this paper, we adopt CF mMIMO to support URLLC in a smart factory. Specifically, we first derive the lower bound (LB) on achievable uplink data rate under the finite blocklength (FBL) with imperfect channel state information (CSI) for both maximum-ratio combining (MRC) and full-pilot zero-forcing (FZF) decoders. The derived LB rates based on the MRC case have the same trends as the ergodic rate, while LB rates using the FZF decoder tightly match the ergodic rates, which means that resource allocation can be performed based on the LB data rate rather the exact ergodic data rate under FBL. The log-function method and successive convex approximation (SCA) are then used to approximately transform the non-convex weighted sum rate problem into a series of geometric program (GP) problems, and an iterative algorithm is proposed to jointly optimize the pilot and payload power allocation. Simulation results demonstrate that CF mMIMO significantly improves the average weighted sum rate (AWSR) compared to centralized mMIMO. An interesting observation is that increasing the number of devices improves the AWSR for CF mMIMO whilst the AWSR remains relatively constant for centralized mMIMO.

This paper investigates the two-timescale transmission scheme for reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) systems, where the beamforming at the base station (BS) is adapted to the rapidly-changing instantaneous channel state information (CSI), while the nearly-passive beamforming at the RIS is adapted to the slowly-changing statistical CSI. Specifically, we first consider a system model with spatially independent Rician fading channels, which leads to tractable expressions and offers analytical insights on the power scaling laws and on the impact of various system parameters. Then, we analyze a more general system model with spatially correlated Rician fading channels and consider the impact of electromagnetic interference (EMI) caused by any uncontrollable sources present in the considered environment. For both case studies, we apply the linear minimum mean square error (LMMSE) estimator to estimate the aggregated channel from the users to the BS, utilize the low-complexity maximal ratio combining (MRC) detector, and derive a closed-form expression for a lower bound of the achievable rate. Besides, an accelerated gradient ascent-based algorithm is proposed for solving the minimum user rate maximization problem. Numerical results show that, in the considered setup, the spatially independent model without EMI is sufficiently accurate when the inter-distance of the RIS elements is sufficiently large and the EMI is mild. In the presence of spatial correlation, we show that an RIS can better tailor the wireless environment. Furthermore, it is shown that deploying an RIS in a massive MIMO network brings significant gains when the RIS is deployed close to the cell-edge users. On the other hand, the gains obtained by the users distributed over a large area are shown to be modest.

In this paper, the intelligent reflecting surface (IRS)-aided unmanned aerial vehicle (UAV) communication system is studied, where the UAV is deployed to serve the user equipment (UE) with the assistance of multiple IRSs mounted on several buildings to enhance the communication quality between UAV and UE. We aim to maximize the energy efficiency of the system, including the data rate of UE and the energy consumption of UAV via jointly optimizing the UAV's trajectory and the phase shifts of reflecting elements of IRS, when the UE moves and the selection of IRSs is considered for the energy saving purpose. Since the system is complex and the environment is dynamic, it is challenging to derive low-complexity algorithms by using conventional optimization methods. To address this issue, we first propose a deep Q-network (DQN)-based algorithm by discretizing the trajectory, which has the advantage of training time. Furthermore, we propose a deep deterministic policy gradient (DDPG)-based algorithm to tackle the case with continuous trajectory for achieving better performance. The experimental results show that the proposed algorithms achieve considerable performance compared to other traditional solutions.

In this paper, the robust design for the intelligent reflective surface (IRS) assisted wireless multi-group multicast system is considered, in which two optimization design problems under two different channel state information (CSI) error models are separately discussed, i.e., the fairness-based problems and the quality-of-service (QoS)-based problems for both the bounded CSI error model and the statistical CSI error model. In order to deal with the non-convex constraints of the considered problems, i.e., bounded CSI error based constraint and statistical CSI error based constraint, S-procedure is adopted to convert the non-convex SINR constraint with bounded CSI error into linear matrix inequalities (LMIs), and the Bernstein-type inequality is utilized to transform the outage probability constraint with statistical CSI error into a second-order cone (SOC) constraint and linear inequalities. Following that, two efficient algorithms based on alternate optimization (AO) are proposed to solve the fairness problems and QoS problems, wherein the semi-definite programming (SDP), penalty convex-concave procedure (CCP) and semi-definite relaxation (SDR) are utilized. Furthermore, we analyze the complexity of the proposed algorithms. Finally, some numerical simulation results are presented to verify the effectiveness of the proposed algorithms, and the impacts of the CSI error and the discrete precision of IRS reflection phase shift on the system performance are analyzed, which provides some insights for the IRS deployment and system robust design.

This paper investigates reconfigurable intelligent surface (RIS)-assisted secure multiuser communication systems in the presence of hardware impairments (HIs) at the RIS and the transceivers. We jointly optimize the beamforming vectors at the base station (BS) and the phase shifts of the reflecting elements at the RIS so as to maximize the weighted minimum approximate ergodic secrecy rate (WMAESR), subject to the transmission power constraints at the BS and unit-modulus constraints at the RIS. To solve the formulated optimization problem, we first decouple it into two tractable subproblems and then use the block coordinate descent (BCD) method to alternately optimize the subproblems. Two different methods are proposed to solve the two obtained subproblems. The first method transforms each subproblem into a second order cone programming (SOCP) problem by invoking the penalty convex–concave procedure (CCP) method and the closed-form fractional programming (FP) criterion, and then directly solves them by using CVX. The second method leverages the minorization-maximization (MM) algorithm. Specifically, we first derive a concave approximation function, which is a lower bound of the original objective function, and then the two subproblems are transformed into two simple surrogate problems that admit closed-form solutions. Simulation results verify the performance gains of the proposed robust transmission methods over existing non-robust designs. In addition, the MM algorithm is shown to have much lower complexity than the SOCP-based algorithm.

The low hardware cost makes ultra-large (XL) reconfigurable intelligent surfaces (RIS) an attractive solution for enabling the intelligent electromagnetic environment, but it brings the challenge of near-field propagation channels. In this paper, we consider the propagation feature of the spherical wavefront in the near field of the millimeter-wave/sub Terahertz (mmWave/subTHz) localization system with the assistance of a RIS. The localization problem is investigated based on the derived second-order Fresnel approximation of the near-field channel model. In addition, the RIS training phase shifts and pilots are carefully designed to increase the channel rank so that the channel covariance matrix can be efficiently estimated. Simulation results validate the proposed near-field channel approximation and the localization algorithm.

In this paper, we develop a two-phase based uplink channel estimation strategy with reduced pilot overhead for an reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) multiple-input multiple-output (MIMO) communication system. Specifically, in Phase I, an OMP-based method is adopted to estimate the AoDs at the users. The remaining parameters including the common AoAs at the BS, the cascaded AoDs at the RIS, and the cascaded channel gains are estimated in Phase II. In particular, the estimation of cascaded AoDs and channel gains can be formulated as 1-sparse recovery problems by decomposing the estimation of a multi-antenna channel with$J$scatterers into estimating$J$single-scatterer channels for virtual single-antenna users. Finally, the theoretical number of pilots required for the proposed method are analyzed and the simulation results are presented to demonstrate the high channel estimation accuracy.

This paper analyzes and optimizes the reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) systems with zero-forcing (ZF) detectors under imperfect channel state information (CSI). We first propose a low-overhead minimum mean square error (MMSE) channel estimator, and then derive and analyze closed-form expressions for the uplink achievable rate. Our analytical results prove that: 1) regardless of the RIS phase shift design, the rate of all users scales at least on the order of $\mathcal{O}\left( {{{\log }_2}(MN)} \right)$, where M and N are the numbers of antennas and reflecting elements, respectively; 2) by aligning the RIS phase shifts to one user, the rate of this user can at most scale on the order of $\mathcal{O}\left( {{{\log }_2}(MN)} \right)$. Furthermore, we propose a low-complexity majorization-minimization (MM)-based algorithm to optimize the sum user rate, where closed-form solutions are obtained in each iteration. Finally, simulation results validate all derived analytical results. Our simulation results also show that the maximum sum rate can be closely approached by simply aligning the RIS phase shifts to an arbitrary user.

This paper investigates the lower bounds on the location estimation error for multiple reconfigurable intelligent surfaces (RISs)-aided millimeter-wave (mmWave) positioning systems. The error lower bound is quantified by Cramer-Rao lower bounds (CRLB), of which two are decisive, namely, the position error bound (PEB), and the rotation error bound (REB). This paper begins by deriving the analytical expressions of the PEB and REB as functions of the RIS phase shifts. Then, the lowest achievable PEB and REB are obtained by optimizing the phase shifts of all RISs using the particle swarm optimization (PSO) algorithm. Numerical results have shown that a three-RIS-aided system generates 38.6% lower PEB and REB with the most basic beam-alignment phase shifts strategy compared to the single-RIS system. With the RIS phase shifts optimized by the PSO algorithm, the PEB and REB can be further reduced by another 41.2%.

This paper studies the worst-case robust beamforming design for a reconfigurable intelligent surface (RIS) aided non-orthogonal multiple access (NOMA) network with imperfect channels. We aim to minimize the transmission power while satisfying the requirement of the worst-case quality of service (QoS). With the worst-case QoS constraints, unit-modulus constraints and imperfect channel state information (CSI), this problem is a non-convex optimization problem. To solve this problem, we propose a two-procedure algorithm by applying penalty function and semidefinite relaxation (SDR). Finally, simulation results illustrate that the RIS-aided NOMA system has better performance than the traditional NOMA system.

In this paper, the sum rate is maximized for the intelligent reflective surface (IRS) assisted terahertz (THz) non-orthogonal multiple access (NOMA) communication system. A novel algorithm is proposed to alternatively optimize the IRS phase shift, the sub-band allocation, and power control. To tackle the formulated non-convex problem, we utilize the auxiliary variables to find the feasible initialization solution meanwhile guarantee the individual rate requirements. The decoding order of successive interference cancellation (SIC) is determined according to channel gain maximization, and the IRS phase is further adjusted to improve the sum rate. A long-distance priority (LDP) algorithm is then proposed to compensate for the distance-dependent THz pathloss attenuation, and a blocking pair eliminating (BPE) algorithm is proposed to obtain a stable THz sub-band allocation. Simulation results show that the proposed scheme significantly enhances the sum-rate performance of the IRS-assisted THz NOMA networks.

In large-scale mobile edge computing (MEC) systems, the task latency, and energy consumption are important for massive resource-consuming and delay-sensitive Internet of Things Devices (IoTDs). Against this background, we propose a distributed intelligent resource scheduling (DIRS) framework to minimize the sum of task latency and energy consumption for all IoTDs, which can be formulated as a mixed-integer nonlinear programming. The DIRS framework includes centralized training relying on the global information and distributed decision making by each agent deployed in each MEC server. Specifically, we first introduce a novel multiagent ensemble-assisted distributed deep reinforcement learning (DRL) architecture, which can simplify the overall neural network structure of each agent by partitioning the state space and also improve the performance of a single agent by combining decisions of all the agents. Second, we apply action refinement to enhance the exploration ability of the proposed DIRS framework, where the near-optimal state-action pairs are obtained by a novel Levy flight search. Finally, an imitation acceleration scheme is presented to pretrain all the agents, which can significantly accelerate the learning process of the proposed framework through learning the professional experience from a small amount of demonstration data. The simulation results in three typical scenarios demonstrate that the proposed DIRS framework is efficient and outperforms the existing benchmark schemes.

In this article, we investigate a reconfigurable intelligent surface (RIS)-aided multiuser full-duplex secure communication system with hardware impairments at transceivers and RIS, where multiple eavesdroppers overhear the two-way transmitted signals simultaneously, and a RIS is applied to enhance the secrecy performance. Aiming at maximizing the sum secrecy rate (SSR), a joint optimization problem of the transmit beamforming at the base station (BS) and the reflecting beamforming at the RIS is formulated under the transmit power constraint of the BS and the unit modulus constraint of the phase shifters. As the environment is time varying and the system is high dimensional, this nonconvex optimization problem is mathematically intractable. A deep reinforcement learning (DRL)-based algorithm is explored to obtain the satisfactory solution by repeatedly interacting with and learning from the dynamic environment. Extensive simulation results illustrate that the DRL-based secure beamforming algorithm is proved to be significantly effective in improving the SSR. It is also found that the performance of the DRL-based method can be greatly improved and the convergence speed of the neural network can be accelerated with appropriate neural network parameters.

In this article, we investigate the energy minimization problem of an unmanned-aerial-vehicle (UAV)-assisted data collection sensor network. We jointly optimize the trajectory of the UAV and the power consumption of the sensors for data uploading with the power and energy constraints of sensors. The trajectory design consists of two parts: 1) the serving orders for sensors and 2) the UAV’s hovering positions, where the latter is highly coupled with the power consumption of the sensors. To find the optimal serving orders of sensors, we formulate the problem as a standard traveling salesman problem (TSP), which can be optimally solved by the efficient Cutting-Plane method. To solve the UAV position and sensor uploading power optimization problem, we propose the PSPSCA algorithm that optimizes the transmit power by the pattern search method, while the UAV’s hovering positions are optimized by the successive-convex-approximation (SCA) method in the inner loop. To deal with the high computational complexity of the PSPSCA algorithm, we analyze the analytical relationship between optimal sensor uploading power and the UAV’s hovering positions, based on which we simplify the optimization problem and propose the AQSCA algorithm as an alternative approach. Simulation results have validated that the proposed algorithm outperforms the existing benchmark schemes.

This paper provides a theoretical framework for understanding the performance of reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) with zero-forcing (ZF) detectors under imperfect channel state information (CSI). We first introduce a low-overhead minimum mean square error (MMSE) channel estimator, and then derive and analyze closed-form expressions for the uplink achievable rate. Our analytical results demonstrate that: 1) regardless of the RIS phase shift design, the rate of all users scales at least on the order of$\mathcal {O}\left ({\log _{2}\left ({MN}\right)}\right)$, where$M$and$N$are the numbers of antennas and reflecting elements, respectively; 2) by aligning the RIS phase shifts to one user, the rate of this user can at most scale on the order of$\mathcal {O}\left ({\log _{2}\left ({MN^{2}}\right)}\right)$; 3) either$M$or the transmit power can be reduced inversely proportional to$N$, while maintaining a given rate. Furthermore, we propose two low-complexity majorization-minimization (MM)-based algorithms to optimize the sum user rate and the minimum user rate, respectively, where closed-form solutions are obtained in each iteration. Finally, simulation results validate the accuracy of all derived analytical results. Our simulation results also show that the maximum sum rate can be closely approached by simply aligning the RIS phase shifts to an arbitrary user.

Optimally extracting the advantages available from reconfigurable intelligent surfaces (RISs) in wireless communications systems requires estimation of the channels to and from the RIS. The process of determining these channels is complicated when the RIS is composed of passive elements without any sensing or data processing capabilities, and thus, the channels must be estimated indirectly by a noncolocated device, typically a controlling base station (BS). In this article, we examine channel estimation for passive RIS-based systems from a fundamental viewpoint. We study various possible channel models and the identifiability of the models as a function of the available pilot data and behavior of the RIS during training. In particular, we will consider situations with and without line-of-sight propagation, single-antenna and multi-antenna configurations for the users and BS, correlated and sparse channel models, single-carrier and wideband orthogonal frequency-division multiplexing (OFDM) scenarios, availability of direct links between the users and BS, exploitation of prior information, as well as a number of other special cases. We further conduct simulations of representative algorithms and comparisons of their performance for various channel models using the relevant Cramér-Rao bounds.

To alleviate the negative impact of noise on wireless federated learning (FL), we propose a channel-weighted aggregation scheme of FL (CWA-FL), in which the parameter server (PS) makes aggregation of the gradients according to the channel conditions of devices. In the proposed scheme, the gradients are transmitted to the PS in an uncoded way through an orthogonal multiple access (OMA) channel, which can avoid the synchronization issue among devices faced by over-the-air FL. The convergence analysis of CWA-FL is conducted and the theoretical results show that the scheme can converge with the rate of$\mathcal {O} (\frac{1}{T})$. Simulation results show that the proposed scheme performs better than the equal-weighted aggregation scheme of FL (EWA-FL) and is more robust to noise.

Reconfigurable intelligent surfaces (RISs) are envisioned to be a disruptive wireless communication technique that is capable of reconfiguring the wireless propagation environment. In this paper, we study a free-space RIS-assisted multiple-input single-output (MISO) communication system in far-field operation. To maximize the received power from the physical and electromagnetic nature point of view, a comprehensive optimization, including beamforming of the transmitter, phase shifts of the RIS, orientation and position of the RIS is formulated and addressed. After exploiting the property of line-of-sight (LoS) links, we derive closed-form solutions of beamforming and phase shifts. For the non-trivial RIS position optimization problem in arbitrary three-dimensional space, a dimensional-reducing theory is proved. The simulation results show that the proposed closed-form beamforming and phase shifts approach the upper bound of the received power. The robustness of our proposed solutions in terms of the perturbation is also verified. Moreover, the RIS significantly enhances the performance of the mmWave/THz communication system.

In this paper, we adopt a three-stage based uplink channel estimation protocol with reduced pilot overhead for an reconfigurable intelligent surface (RIS)-aided multi-user (MU) millimeter wave (mmWave) communication system, in which both the base station (BS) and the RIS are equipped with a uniform planar array (UPA). Specifically, in Stage I, the channel state information (CSI) of a typical user is estimated. To address the power leakage issue for the common angles-of-arrival (AoAs) estimation in this stage, we develop a low-complexity one-dimensional search method. In Stage II, a re-parameterized common BS-RIS channel is constructed with the estimated information from Stage I to estimate other users’ CSI. In Stage III, only the rapidly varying channel gains need to re-estimated. Furthermore, the proposed method can be extended to multi-antenna UPA-type users, by decomposing the estimation of a multi-antenna channel with$J$scatterers into estimating$J$single-scatterer channels for a virtual single-antenna user. An orthogonal matching pursuit (OMP)-based method is proposed to estimate the angles-of-departure (AoDs) at the users. Simulation results demonstrate that the proposed algorithm significantly achieves high channel estimation accuracy, which approaches the genie-aided upper bound in the high signal-to-noise ratio (SNR) regime.

Different from conventional wired line connections, industrial control through wireless transmission is widely regarded as a promising solution due to its reduced cost, increased long-term reliability, and enhanced reliability. However, mission-critical applications impose stringent quality of service (QoS) requirements that entail ultra-reliability low-latency communications (URLLC). The primary feature of URLLC is that the blocklength of channel codes is short, and the conventional Shannon’s Capacity is not applicable. In this paper, we consider the URLLC in a factory automation (FA) scenario. Due to densely deployed equipment in FA, wireless signal are easily blocked by the obstacles. To address this issue, we propose to deploy intelligent reflecting surface (IRS) to create an alternative transmission link, which can enhance the transmission reliability. In this paper, we focus on the performance analysis for IRS-aided URLLC-enabled communications in a FA scenario. Both the average data rate (ADR) and the average decoding error probability (ADEP) are derived under finite channel blocklength for seven cases: 1) Rayleigh fading channel; 2) With direct channel link; 3) Nakagami-m fading channel; 4) Imperfect phase alignment; 5) Multiple-IRS case; 6) Rician fading channel; 7) Correlated channels. Extensive numerical results are provided to verify the accuracy of our derived results.

This paper considers an uplink reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) system, where the phase shifts of the RIS are designed relying on statistical channel state information (CSI). Considering the complex environment, the general Rician channel model is adopted for both the users-RIS links and RIS-BS links. We first derive the closed-form approximate expressions for the achievable rate which holds for arbitrary numbers of base station (BS) antennas and RIS elements. Then, we utilize the derived expressions to provide some insights, including the asymptotic rate performance, the power scaling laws, and the impacts of various system parameters on the achievable rate. We also tackle the sum-rate maximization and the minimum user rate maximization problems by optimizing the phase shifts at the RIS based on genetic algorithm (GA). Finally, extensive simulations are provided to validate the benefits by integrating RIS into conventional massive MIMO systems. Our simulations also demonstrate the feasibility of deploying large-size but low-resolution RIS in massive MIMO systems.

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.

This paper proposes a new simultaneous terahertz (THz) information and power transfer (STIPT) system, which utilizes a reconfigurable intelligent surface (RIS) for both the data and power transmission. We aim to maximize the information users’ (IUs’) sum data rate while guaranteeing the power harvesting requirements of energy users (EUs) and RIS. To solve the formulated non-convex problem, the block coordinate descent (BCD) based algorithm is adopted to alternately optimize the transmit precoding of IUs, RIS’s reflecting coefficients, and the position of RIS. Additionally, the penalty constrained convex approximation (PCCA) algorithm is proposed to optimize the deployment of the RIS, where the introduced penalties ensure that the solution is always feasible. The simulation results show that the proposed solution outperforms the benchmark schemes, and the proposed BCD algorithm can greatly improve the performance of the STIPT system.

Intelligent reflecting surface (IRS), consisting of low-cost passive elements, is a promising technology for improving the spectral and energy efficiency of the fifth-generation (5G) and beyond networks. It is also noteworthy that an IRS can shape the reflected signal propagation. Most works in IRS-assisted systems have ignored the impact of the inevitable residual hardware impairments (HWIs) at both the transceiver hardware and the IRS while any relevant works have addressed only simple scenarios, e.g., with single-antenna network nodes and/or without taking the randomness of phase noise at the IRS into account. In this work, we aim at filling up this gap by considering a general IRS-assisted multi-user (MU) multiple-input single-output (MISO) system with imperfect channel state information (CSI) and correlated Rayleigh fading. In parallel, we present a general computationally efficient methodology for IRS reflecting beamforming (RB) optimization. Specifically, we introduce an advantageous channel estimation (CE) method for such systems accounting for the HWIs. Moreover, we derive the uplink achievable spectral efficiency (SE) with maximal-ratio combining (MRC) receiver, displaying three significant advantages being: 1) its closed-form expression, 2) its dependence only on large-scale statistics, and 3) its low training overhead. Notably, by exploiting the first two benefits, we achieve to perform optimization with respect to the RB that can take place only per several coherence intervals, and thus, reduces significantly the computational cost compared to other methods based on instantaneous CSI which require frequent phase optimization. Among the insightful observations, we highlight that the unrealistic assumption of uncorrelated Rayleigh fading does not allow optimization of the SE, which makes the application of an IRS ineffective. Also, in the case that the phase drifts, describing the distortion of the phases in the RBM, are uniformly distributed, the presence of an IRS provides no advantage. The analytical results outperform previous works and are verified by Monte-Carlo (MC) simulations.

Beamforming design is crucial to reconfigurable intelligent surface (RIS)-aided communication networks. However, most of the existing works assume ideal hardware and perfect channel state information (CSI), which are unrealistic assumptions in practical systems. In order to improve system robustness and user fairness, in this paper, we firstly study the max-min energy efficiency problem for RIS-aided heterogeneous networks under non-ideal hardware and imperfect CSI. Specifically, the joint optimization of transmit beamforming vectors of femto base stations (FBSs) and the phase shift matrices of RISs is formulated as a nonconvex problem to maximize the minimum energy efficiency of femtocells subject to the constraints of the maximum transmit power of FBSs, the maximum cross-tier interference power of macrocell users, the minimum rates of femtocell users, and unit modulus of RISs. To facilitate the design, we develop an iterative block coordinate descent-based algorithm which exploits the semidefinite relaxation, the S-procedure, the successive convex approximation method, and the singular value decomposition method. Simulation results demonstrate the superiority of the proposed algorithm.

The implementation of full-duplex (FD) theoretically doubles the spectral efficiency of cellular communications. We propose a multiuser FD cellular network relying on an intelligent reflecting surface (IRS). The IRS is deployed to cover a dead zone while suppressing user-side self-interference (SI) and co-channel interference (CI) by carefully tuning the phase shifts of its massive low-cost passive reflection elements. To ensure network fairness, we aim to maximize the weighted minimum rate (WMR) of all users by jointly optimizing the precoding matrix of the base station (BS) and the reflection coefficients of the IRS. Specifically, we propose a low-complexity minorization-maximization (MM) algorithm for solving the subproblems of designing the precoding matrix and the reflection coefficients, respectively. Simulation results confirm the convergence and efficiency of our proposed algorithm, and validate the advantages of introducing IRS to realize FD cellular communications.

A fundamental challenge for millimeter wave (mmWave) communications lies in its sensitivity to the presence of blockages, which impact the connectivity of the communication links and ultimately the reliability of the entire network. In this paper, we are exploited to deal with the link outage issue caused by a reconfigurable intelligent surface (RIS)-aided mmWave communication system for enhancing the network reliability and connectivity in the presence of random blockages. To enhance the robustness of the beamforming in the presence of random blockages, we formulate a stochastic optimization problem with the aim of minimizing the outage probability. To tackle the proposed optimization problem, we introduce a low-complexity algorithm based on the stochastic majorization-minimization method, which learns sensible blockage patterns without searching for all combinations of potentially blocked links. Numerical results confirm the performance benefits of the proposed algorithm in terms of outage probability and effective data rate.

This paper investigates a UAV-enabled wireless downlink system, where a UAV-enabled base station flies above a group of Internet of things devices (IoTDs) and transmits data to them. In order to ensure the fairness among all IoTDs, we jointly optimize scheduling association and UAV trajectory to maximize the minimal throughput of all IoTDs under the 3dimensional (3-D) channel model. Meanwhile, to ensure the stability of communication, we aim to guarantee a high probability of line-of-sight (LoS) links between the UAV and all IoTDs. The optimization problem is non-convex and we propose an iterative algorithm based on the block coordinate descent and successive convex approximation to solve it. Furthermore, we show the convergence of the algorithm through simulations. It is shown that the proposed algorithm achieves higher data rate than the traditional scheme without guaranteeing the LoS probability.

Queuing delay is of essential importance in the Internet-of-Things scenarios where the buffer sizes of devices are limited. The existing cross-layer research contributions aiming at minimizing the queuing delay usually rely on either transmit power control or dynamic spectrum allocation. Bearing in mind that the transmission throughput is dependent on the distance between the transmitter and the receiver, in this context we exploit the agility of the unmanned-aerial-vehicle (UAV)-mounted base stations (BSs) for proactively adjusting the aerial BS (ABS)’s placement in accordance with wireless teletraffic dynamics. Specifically, we formulate a minimum-delay ABS placement problem for UAV-enabled networks, subject to realistic constraints on the ABS’s battery life and velocity. Its solutions are technically realized under three different assumptions in regard to the wireless teletraffic dynamics. The backward induction technique is invoked for both the scenario where the full knowledge of the wireless teletraffic dynamics is available, and for the case where only their statistical knowledge is available. In contrast, a reinforcement learning aided approach is invoked for the case when neither the exact number of arriving packets nor that of their statistical knowledge is available. The numerical results demonstrate that our proposed algorithms are capable of improving the system’s performance compared to the benchmark schemes in terms of both the average delay and of the buffer overflow probability.

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 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.

In this paper, we propose a low-latency virtual reality (VR) delivery system where an unmanned aerial vehicle (UAV) base station (U-BS) is deployed to deliver VR content from a cloud server to multiple ground VR users. Each VR input data requested by the VR users can be either projected at the U-BS before transmission or processed locally at each user. Popular VR input data is cached at the U-BS to further reduce backhaul latency from the cloud server. For this system, we design a low-complexity iterative algorithm to minimize the maximum communications and computing latency among all VR users subject to the computing, caching and transmit power constraints, which is guaranteed to converge. Numerical results indicate that our proposed algorithm can achieve a lower latency compared to other benchmark schemes. Moreover, we observe that the maximum latency mainly comes from communication latency when the bandwidth resource is limited, while it is dominated by computing latency when computing capacity is low. In addition, we find that caching is helpful to reduce latency.

Wireless powered mobile edge computing (WP-MEC) has been recognized as a promising technique to provide both enhanced computational capability and sustainable energy supply to massive low-power wireless devices. However, its energy consumption becomes substantial, when the transmission link used for wireless energy transfer (WET) and for computation offloading is hostile. To mitigate this hindrance, we propose to employ the emerging technique of intelligent reflecting surface (IRS) in WP-MEC systems, which is capable of providing an additional link both for WET and for computation offloading. Specifically, we consider a multi-user scenario where both the WET and the computation offloading are based on orthogonal frequency-division multiplexing (OFDM) systems. Built on this model, an innovative framework is developed to minimize the energy consumption of the IRS-aided WP-MEC network, by optimizing the power allocation of the WET signals, the local computing frequencies of wireless devices, both the sub-band-device association and the power allocation used for computation offloading, as well as the IRS reflection coefficients. The major challenges of this optimization lie in the strong coupling between the settings of WET and of computing as well as the unit-modules constraint on IRS reflection coefficients. To tackle these issues, the technique of alternating optimization is invoked for decoupling the WET and computing designs, while two sets of locally optimal IRS reflection coefficients are provided for WET and for computation offloading separately relying on the successive convex approximation method. The numerical results demonstrate that our proposed scheme is capable of monumentally outperforming the conventional WP-MEC network without IRSs. Quantitatively, about 80% energy consumption reduction is attained over the conventional MEC system in a single cell, where 3 wireless devices are served via 16 sub-bands, with the aid of an IRS comprising of 50 elements.

In this paper, we investigate the design of robust and secure transmission in intelligent reflecting surface (IRS) aided wireless communication systems. In particular, a multi-antenna access point (AP) communicates with a single-antenna legitimate receiver in the presence of multiple single-antenna eavesdroppers, where the artificial noise (AN) is transmitted to enhance the security performance. Besides, we assume that the cascaded AP-IRS-user channels are imperfect due to the channel estimation error. To minimize the transmit power, the beamforming vector at the transmitter, the AN covariance matrix, and the IRS phase shifts are jointly optimized subject to the outage rate probability constraints under the statistical cascaded channel state information (CSI) error model. To handle the resulting non-convex optimization problem, we first approximate the outage rate probability constraints by using the Bernstein-type inequality. Then, we develop a suboptimal algorithm based on alternating optimization, the penalty-based and semidefinite relaxation methods. Simulation results reveal that the proposed scheme significantly reduces the transmit power compared to other benchmark schemes.

This paper investigates a symbiotic unmanned aerial vehicle (UAV)-assisted intelligent reflecting surface (IRS) radio system, where the UAV is leveraged to help the IRS reflect its own signals to the base station, and meanwhile enhance the UAV transmission by passive beamforming at the IRS. First, we consider the weighted sum bit error rate (BER) minimization problem among all IRSs by jointly optimizing the UAV trajectory, IRS phase shift matrix, and IRS scheduling, subject to the minimum primary rate requirements. To tackle this complicated problem, a relaxation-based algorithm is proposed. We prove that the converged relaxation scheduling variables are binary, which means that no reconstruct strategy is needed, and thus the UAV rate constraints are automatically satisfied. Second, we consider the fairness BER optimization problem. We find that the relaxation-based method cannot solve this fairness BER problem since the minimum primary rate requirements may not be satisfied by the binary reconstruction operation. To address this issue, we first transform the binary constraints into a series of equivalent equality constraints. Then, a penalty-based algorithm is proposed to obtain a suboptimal solution. Numerical results are provided to evaluate the performance of the proposed designs under different setups, as compared with benchmarks.

Intelligent reflecting surface (IRS) has emerged as an appealing solution to enhance wireless communication performance by reconfiguring the wireless propagation environment. In this paper, we propose to apply IRS to the physical-layer service integration (PHY-SI) system, where a single-antenna access point (AP) integrates two sorts of service messages, i.e., multicast message and confidential message, via superposition coding to serve multiple single-antenna users. Our goal is to optimize the power allocation (for transmitting different messages) at the AP and the passive beamforming at the IRS to maximize the achievable secrecy rate region. To this end, we formulate this problem as a bi-objective optimization problem, which is shown equivalent to a secrecy rate maximization problem subject to the constraints on the quality of multicast service. Due to the non-convexity of this problem, we propose two customized algorithms to obtain its high-quality suboptimal solutions, thereby approximately characterizing the secrecy rate region. The resulting performance gap with the globally optimal solution is analyzed. Furthermore, we provide theoretical analysis to unveil the impact of IRS beamforming on the performance of PHY-SI. Numerical results demonstrate the advantages of leveraging IRS in improving the performance of PHY-SI and also validate our theoretical analysis.

In this paper, a cooperative task computation framework exploits the computation resource in user equipments (UEs) to accomplish more tasks meanwhile minimizes the power consumption of UEs. The system cost includes the cost of UEs' power consumption and the penalty of unaccomplished tasks, and the system cost is minimized by jointly optimizing binary offloading decisions, the computational frequencies, and the offloading transmit power. To solve the formulated mixed-integer non-linear programming problem, three efficient algorithms are proposed, i.e., integer constraints relaxation-based iterative algorithm (ICRBI), heuristic matching algorithm, and the decentralized algorithm. The ICRBI algorithm achieves the best performance at the cost of the highest complexity, while the heuristic matching algorithm significantly reduces the complexity while still providing reasonable performance. As the previous two algorithms are centralized, the decentralized algorithm is also provided to further reduce the complexity, and it is suitable for the scenarios that cannot provide the central controller. The simulation results are provided to validate the performance gain in terms of the total system cost obtained by the proposed cooperative computation framework.

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

In this paper, we investigate the robust transmission design for the intelligent reflecting surface (IRS) aided secure wireless communication systems, where a multi-antenna access point (AP) communicates with a single-antenna legitimate receiver in the presence of multiple single-antenna eavesdroppers via IRS. The estimation error of the imperfect cascaded AP-IRS-user channels is considered in the robust beamforming. Specifically, a transmit power minimization problem is formulated subject to the outage rate probability of information leakage to Eves under the statistical cascaded CSI error model, and the beamformer at the transmitter, the covariance matrix of artificial noise (AN), and the IRS phase shifts are jointly optimized. To handle the resulting non-convex optimization problem, we first approximate the rate outage probability constraints by using the Bernstein-type inequality. Then, we develop a suboptimal algorithm based on the alternating optimization, penalty-based and semidefinite relaxation methods. Simulation results reveal that the proposed scheme significantly reduces the transmit power while ensures the system security.

In this paper, we study the resource allocation for a secure mission-critical IoT communication system with URLLC, where the security capacity formula under finite blocklength is adopted. In specific, we jointly optimize the power and channel bandwidth unit allocation to minimize the system power consumption subject to each device's security capacity requirement and total available channel bandwidth. We express the power for each device as a function of channel bandwidth unit, and equivalently transform the original problem into a channel bandwidth unit allocation problem. By relaxing the discrete variables into continuous ones, a sufficient condition when the transformed problem is a convex problem is provided. Efficient method is proposed to solve the problem. Simulation results confirm the performance advantage of our proposed algorithm over the benchmark method.

Vehicular communication is an important application of the fifth generation of mobile communication systems (5G). Due to its low cost and energy efficiency, intelligent reflecting surface (IRS) has been envisioned as a promising technique that can enhance the coverage performance significantly by passive beamforming. In this paper, we analyze the outage probability performance in IRS-assisted vehicular communication networks. We derive the expression of outage probability by utilizing series expansion and central limit theorem. Numerical results show that the IRS can significantly reduce the outage probability for vehicles in its vicinity. The outage probability is closely related to the vehicle density and the number of IRS elements, and better performance is achieved with more reflecting elements.

Intelligent reflecting surface (IRS) has been proved to be an efficient technology to improve the spectrum and energy efficiency in cognitive radio (CR) networks. Unfortunately, due to the fact that the primary users (PUs) and the secondary users (SUs) are non-cooperative, it is challenging to obtain the perfect PUs-related channel sate information (CSI). In this paper, we investigate the robust beamforming design based on the statistical CSI error model for PU-related cascaded channels in IRS-aided CR systems. We jointly optimize the transmit precoding (TPC) matrix and phase shifts to minimize the SU's total transmit power, meanwhile subject to the quality of service (QoS) of SUs, the interference imposed on the PU and unit-modulus of the reflective beamforming. The non-convex optimization problems are transformed into two second-order cone programming (SOCP) subproblems and efficient algorithms are proposed for solving these subproblems. Simulation results verify the efficiency of the proposed algorithms and reveal the impacts of CSI uncertainties on ST's transmit power and feasibility rate of the optimization problem.

Intelligent reflection surface (IRS) has recently been recognized as a promising technique to enhance the performance of wireless systems due to its ability of reconfiguring the signal propagation environment. However, the perfect channel state information (CSI) is challenging to obtain at the base station (BS) due to the lack of radio frequency (RF) chains at the IRS. Since most of the existing channel estimation methods were developed to acquire the cascaded BS-IRS-user channels, this paper is the first work to study the robust beamforming based on the imperfect cascaded BS-IRS-user channels at the transmitter (CBIUT). Specifically, the transmit power minimization problems are formulated subject to the rate outage probability constraints under the statistical CSI error model, respectively. After approximating the rate outage probability constraints by using the Bernstein-type inequality, the reformulated problems can be efficiently solved. Numerical results show that the negative impact of the CBIUT error on the system performance is greater than that of the direct CSI error.

Power constraints and channel acquisition pose practical challenges in multi-beam satellite communications. Motivated by this, we investigate robust energy-efficient multigroup multicast beamforming in multi-beam satellite communications with full frequency reuse in this paper. Specifically, we consider the problem of minimizing the total power while guaranteeing that the energy efficiency (EE) of each group is above a prescribed threshold. The considered problem is challenging in the sense that the average rates in the definition of the EE generally do not admit an explicit expression and the optimization problem is NP-hard and nonconvex. To tackle this problem, we first adopt a closed-form tight approximation for the average rates. Then the semidefinite relaxation and the concave-convex procedure are utilized to transfer the nonconvex problem into a convex problem. Finally, based on the ranks of the solutions, the eigenvalue decomposition or the Gaussian randomization approach is invoked to generate the final feasible solutions. Numerical results validate the high accuracy of the average rate approximation, and demonstrate that our proposed robust approach significantly outperforms the conventional one, especially for the case with large channel phase error variances.

This paper investigates the capacity region of a discrete memoryless (DM) multiple access wiretap (MAC-WT) channel where, besides confidential messages, the users have also open messages to transmit. All these messages are intended for the legitimate receiver but only the confidential messages need to be protected from the eavesdropper. By using random coding, we find an achievable secrecy rate region, within which perfect secrecy can be realized, i.e., all users can communicate with the legitimate receiver with arbitrarily small probability of error, while the confidential information leaked to the eavesdropper tends to zero.

An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the Internet-of-Things (IoT) users, by optimizing offloading decision, transmission power, and resource allocation in the large-scale mobile-edge computing (MEC) system. Toward this end, a deep reinforcement learning (DRL)-based solution is proposed, which includes the following components. First, a related and regularized stacked autoencoder (2r-SAE) with unsupervised learning is applied to perform data compression and representation for high-dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Second, we present an adaptive simulated annealing approach (ASA) as the action search method of DRL, in which an adaptive ${h}$ -mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Third, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. The numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks.

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.

Computation off-loading in mobile edge computing (MEC) systems constitutes an efficient paradigm of supporting resource-intensive applications on mobile devices. However, the benefit of MEC cannot be fully exploited, when the communications link used for off-loading computational tasks is hostile. Fortunately, the propagation-induced impairments may be mitigated by intelligent reflecting surfaces (IRS), which are capable of enhancing both the spectral- and energy-efficiency. Specifically, an IRS comprises an IRS controller and a large number of passive reflecting elements, each of which may impose a phase shift on the incident signal, thus collaboratively improving the propagation environment. In this paper, the beneficial role of IRSs is investigated in MEC systems, where single-antenna devices may opt for off-loading a fraction of their computational tasks to the edge computing node via a multi-antenna access point with the aid of an IRS. Pertinent latency-minimization problems are formulated for both single-device and multi-device scenarios, subject to practical constraints imposed on both the edge computing capability and the IRS phase shift design. To solve this problem, the block coordinate descent (BCD) technique is invoked to decouple the original problem into two subproblems, and then the computing and communications settings are alternatively optimized using low-complexity iterative algorithms. It is demonstrated that our IRS-aided MEC system is capable of significantly outperforming the conventional MEC system operating without IRSs. Quantitatively, about 20 % computational latency reduction is achieved over the conventional MEC system in a single cell of a 300 m radius and 5 active devices, relying on a 5-antenna access point.

An intelligent reflecting surface (IRS) is invoked for enhancing the energy harvesting performance of a simultaneous wireless information and power transfer (SWIPT) aided system. Specifically, an IRS-assisted SWIPT system is considered, where a multi-antenna aided base station (BS) communicates with several multi-antenna assisted information receivers (IRs), while guaranteeing the energy harvesting requirement of the energy receivers (ERs). To maximize the weighted sum rate (WSR) of IRs, the transmit precoding (TPC) matrices of the BS and passive phase shift matrix of the IRS should be jointly optimized. To tackle this challenging optimization problem, we first adopt the classic block coordinate descent (BCD) algorithm for decoupling the original optimization problem into several subproblems and alternately optimize the TPC matrices and the phase shift matrix. For each subproblem, we provide a low-complexity iterative algorithm, which is guaranteed to converge to the Karush-Kuhn-Tucker (KKT) point of each subproblem. The BCD algorithm is rigorously proved to converge to the KKT point of the original problem. We also conceive a feasibility checking method to study its feasibility. Our extensive simulation results confirm that employing IRSs in SWIPT beneficially enhances the system performance and the proposed BCD algorithm converges rapidly, which is appealing for practical applications.

The Fourth Industrial Revolution (Industrial 4.0) is coming, and this revolution will fundamentally enhance the way factories manufacture products. The conventional wired lines connecting central controller to robots or actuators will be replaced by wireless communication networks due to its low cost of maintenance and high deployment flexibility. However, some critical industrial applications require ultra-high reliability and low latency communication (URLLC). In this paper, we advocate the adoption of massive multiple-input multiple output (MIMO) to support the wireless transmission for industrial applications as it can provide deterministic communications similar as wired lines thanks to its channel hardening effects. To reduce the latency, the channel blocklength for packet transmission is finite, which incurs transmission rate degradation and decoding error probability. Thus, conventional resource allocation for massive MIMO transmission based on Shannon capacity assuming the infinite channel blocklength is no longer optimal. We first derive the closed-form expression of lower bound (LB) of achievable uplink data rate for massive MIMO system with imperfect channel state information (CSI) for both maximum-ratio combining (MRC) and zero-forcing (ZF) receivers. Then, we propose novel low complexity algorithms to solve the achievable data rate maximization problems by jointly optimizing the pilot and payload transmission power for both MRC and ZF. Simulation results confirm the rapid convergence speed and performance advantage over the existing benchmark algorithms.

This article considers an artificial noise (AN)-aided secure MIMO wireless communication system. To enhance the system security performance, the advanced intelligent reflecting surface (IRS) is invoked, and the base station (BS), legitimate information receiver (IR) and eavesdropper (Eve) are equipped with multiple antennas. With the aim for maximizing the secrecy rate (SR), the transmit precoding (TPC) matrix at the BS, covariance matrix of AN and phase shifts at the IRS are jointly optimized subject to constrains of transmit power limit and unit modulus of IRS phase shifts. Then, the secrecy rate maximization (SRM) problem is formulated, which is a non-convex problem with multiple coupled variables. To tackle it, we propose to utilize the block coordinate descent (BCD) algorithm to alternately update the variables while keeping SR non-decreasing. Specifically, the optimal TPC matrix and AN covariance matrix are derived by Lagrangian multiplier method, and the optimal phase shifts are obtained by Majorization-Minimization (MM) algorithm. Since all variables can be calculated in closed form, the proposed algorithm is very efficient. We also extend the SRM problem to the more general multiple-IRs scenario and propose a BCD algorithm to solve it. Simulation results validate the effectiveness of system security enhancement via an IRS.

Ultra-reliable low latency communication (URLLC) is one of three primary use cases in the fifth-generation (5G) networks, and its research is still in its infancy due to its stringent and conflicting requirements in terms of extremely high reliability and low latency. To reduce latency, the channel blocklength for packet transmission is finite, which incurs transmission rate degradation and higher decoding error probability. In this case, conventional resource allocation based on Shannon capacity achieved with infinite blocklength codes is not optimal. Security is another critical issue in mission-critical internet of things (IoT) communications, and physical-layer security is a promising technique that can ensure the confidentiality for wireless communications as no additional channel uses are needed for the key exchange as in the conventional upper-layer cryptography method. This paper is the first work to study the resource allocation for a secure mission-critical IoT communication system with URLLC. Specifically, we adopt the security capacity formula under finite blocklength and consider two optimization problems: weighted throughput maximization problem and total transmit power minimization problem. Each optimization problem is non-convex and challenging to solve, and we develop efficient methods to solve each optimization problem. Simulation results confirm the fast convergence speed of our proposed algorithm and demonstrate the performance advantages over the existing benchmark algorithms.

In this paper, we propose a secure unmanned aerial vehicle (UAV) mobile edge computing (MEC) system where multiple ground users offload large computing tasks to a nearby legitimate UAV in the presence of multiple eavesdropping UAVs with imperfect locations. To enhance security, jamming signals are transmitted from both the full-duplex legitimate UAV and non-offloading ground users. For this system, we design a low-complexity iterative algorithm to maximize the minimum secrecy capacity subject to latency, minimum offloading and total power constraints. Specifically, we jointly optimize the UAV location, users' transmit power, UAV jamming power, offloading ratio, UAV computing capacity, and offloading user association. Numerical results show that our proposed algorithm significantly outperforms baseline strategies over a wide range of UAV self-interference (SI) efficiencies, locations and packet sizes of ground users. Furthermore, we show that there exists a fundamental tradeoff between the security and latency of UAV-enabled MEC systems which depends on the UAV SI efficiency and total UAV power constraints.

Device-to-Device (D2D) communications incorporated with content caching have been regarded as a promising way to offload the cellular traffic data. In this paper, the caching strategy is investigated to maximize the D2D offloading gain with the comprehensive consideration of user collaborative characteristics as well as the physical transmission conditions. Specifically, for a given content, the number of interested users in different groups is different, and users always ask the most trustworthy user in proximity for D2D transmissions. An analytical expression of the D2D success probability is first derived, which represents the probability that the received signal to interference ratio is no less than a given threshold. As the formulated problem is nonconvex, the optimal caching strategy for the special unbiased case is derived in a closed form, and a numerical searching algorithm is proposed to obtain the globally optimal solution for the general case. To reduce the computational complexity, an iterative algorithm based on the asymptotic approximation of the D2D success probability is proposed to obtain the solution that satisfies the Karush-Kuhn-Tucker conditions. The simulation results verify the effectiveness of the analytical results and show that the proposed algorithm outperforms the existing schemes in terms of offloading gain.

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.

Ultra-reliable and low-latency communication (URLLC) is one of three pillar applications defined in the fifth generation new radio (5G NR), and its research is still in its infancy due to the difficulties in guaranteeing extremely high reliability (say 10-9packet loss probability) and low latency (say 1 ms) simultaneously. In URLLC, short packet transmission is adopted to reduce latency, such that conventional Shannon's capacity formula is no longer applicable, and the achievable data rate in finite blocklength becomes a complex expression with respect to the decoding error probability and the blocklength. To provide URLLC service in a factory automation scenario, we consider that the central controller transmits different packets to a robot and an actuator, where the actuator is located far from the controller, and the robot can move between the controller and the actuator. In this scenario, we consider four fundamental downlink transmission schemes, including orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA), relay-assisted, and cooperative NOMA (C-NOMA) schemes. For all these transmission schemes, we aim for jointly optimizing the blocklength and power allocation to minimize the decoding error probability of the actuator subject to the reliability requirement of the robot, the total energy constraints, as well as the latency constraints. We further develop low-complexity algorithms to address the optimization problems for each transmission scheme. For the general case with more than two devices, we also develop a low-complexity efficient algorithm for the OMA scheme. Our results show that the relay-assisted transmission significantly outperforms the OMA scheme, while the NOMA scheme performs well when the blocklength is very limited. We further show that the relay-assisted transmission has superior performance over the C-NOMA scheme due to larger feasible region of the former scheme.

In this paper, the user cooperative task computation is explored by sharing the computing capability of the user equipments (UEs) so as to enhance the performance of mobile edge computing (MEC) networks. The number of completed tasks is maximized while minimizing the total power consumption of the UEs by jointly optimizing the user task offloading decision, the computational speed for the offloaded task and the transmit power for task offloading. An iterative algorithm based on the linear programming relaxation is proposed to solve the formulated mixed integer non-linear problem. The simulation results show that the proposed user cooperative computation scheme can achieve a higher completed tasks ratio than the non-cooperative scheme.

Ultra-reliable and low-latency communication (URLLC) is one of three pillar applications that should be supported by the fifth generation (5G) communications. The research on this topic is still in its infancy due to the difficulties in guaranteeing extremely high reliability (say 10-9) and low latency (say 1 ms) simultaneously. The achievable data rate under the short packet transmission is a complicated function of the transmission power, the blocklength and the decoding error probability. In this paper, we consider resource allocation problem in a factory automation scenario, where the central controller aims for transniitting different packets to two devices (e.g., a robot and an actuator). Two transmission schemes are considered: orthogonal multiple access (OMA) and relay-assisted transmission. We aim to jointly optimize the blocklength and power allocation to minimize the error probability of the actuator subject to reliability requirement of the robot as well as the latency constraints. We develop low-complexity algorithms to address the optimization problems for each transmission scheme. Simulation results demonstrate that the relay-assisted transmission significantly outperforms the OMA scheme.

Unmanned aerial vehicles (UAVs) have been widely used in both military and civilian applications. Equipped with diverse communication payloads, UAVs cooperating with satellites and base stations constitute a space-air-ground three-tier heterogeneous network, which are beneficial in terms of both providing the seamless coverage as well as of improving the capacity for increasingly prosperous Internet of Things networks. However, cross-tier interference may be inevitable among these tightly embraced heterogeneous networks when sharing the same spectrum. The power association problem in satellite, UAV and macrocell three-tier networks becomes a critical issue. In this paper, we propose a two-stage joint hovering altitude and power control solution for the resource allocation problem in UAV networks considering the inevitable cross-tier interference from space-air-ground heterogeneous networks. Furthermore, Lagrange dual decomposition and concave-convex procedure method are used to solve this problem, followed by a low-complexity greedy search algorithm. Finally, simulation results show the effectiveness of our proposed two-stage joint optimization algorithm in terms of UAV network's total throughput.

This paper investigates an uplink non-orthogonal multiple access (NOMA)-based mobile-edge computing (MEC) network. Our objective is to minimize a linear combination of the completion time of all users’ tasks and the total energy consumption of all users including transmission energy and local computation energy subject to computation latency, uploading data rate, time sharing and edge cloud capacity constraints. This work can significantly improve the energy efficiency and end-to-end delay of the applications in future wireless networks. For the general minimization problem, it is first transformed into an equivalent form. Then, an iterative algorithm is accordingly proposed, where closed-form solution is obtained in each step. For the special case with only minimizing the completion time, we propose a bisection-based algorithm to obtain the optimal solution. Also for the special case with infinite cloud capacity, we show that the original minimization problem can be transformed into an equivalent convex one. Numerical results show the superiority of the proposed algorithms compared with conventional algorithms in terms of completion time and energy consumption.

In recent studies, a simple non-coherent communication scheme based on energy detection is proposed in massive single-input multiple-output (SIMO) systems. Before data transmission, the transmitter sends pilots to the receiver for the purpose of estimating the channel statistics. However, this training phase unintentionally provides opportunity for a malicious jammer to attack legitimate communication. In order to secure the legitimate communication, this paper proposes a jamming detection method in non-coherent SIMO systems, in which the information of channel statistics is not required. First, the transmitter sends pilots to the receiver, then the receiver sends the conjugate of its received signal (which may contain jammer signal) back to the transmitter, where the final decision on jamming detection is made. According to the likelihood ratio test principle, two detectors based on variance and standard variance normalization are proposed. The performance analysis indicates that these two detectors are of similar detection performance but of different complexity. Furthermore, it is revealed that the probability of detection initially grows with the number of receive antennas but converges quickly then, whereas the channel statistics from the jammer to the receiver always greatly influences the performance. Finally, the numerical simulations are carried out to validate the proposed detection method.

The ultra-dense cloud radio access network (UD-CRAN), in which remote radio heads are densely deployed in the network, is considered. To reduce the channel estimation overhead, we focus on the design of robust transmit beamforming for user-centric frequency division duplex UD-CRANs, where only limited channel state information (CSI) is available. Specifically, we conceive a complete procedure for acquiring the CSI that includes two key steps: channel estimation and channel quantization. The phase ambiguity (PA) is also quantized for coherent cooperative transmission. Based on the imperfect CSI, we aim to optimize the beamforming vectors in order to minimize the total transmit power subject to the users' rate requirements and fronthaul capacity constraints. We derive the closed-form expression of the achievable data rate by exploiting the statistical properties of multiple uncertain terms. Then, we propose a low-complexity iterative algorithm for solving this problem based on the successive convex approximation technique. In each iteration, the Lagrange dual-decomposition method is employed for obtaining the optimal beamforming vector. Furthermore, a pair of low-complexity user selection algorithms is provided to guarantee the feasibility of the problem. The simulation results confirm the accuracy of our robust algorithm in terms of meeting the rate requirements. Finally, our simulation results verify that using a single bit for quantizing the PA achieves good performance.

The weighted sum-rate maximization problem of ultra-dense cloud radio access networks is considered. The user-centric clustering is adopted for reducing the complexity. To reduce the training overhead, one only needs to estimate the intra-cluster channel-state information (CSI), while only the large-scale channel gains are available outside the cluster. We first derive the rate lower bound (LB) relying on Jensen's inequality. For the special case of non-overlapping clusters, the accurate data rate expression is derived in the closed form. The simulation results show the tightness of the LB for both the overlapped and non-overlapped cases. Then, we consider an alternative problem where the actual data rate is replaced by its LB, which constitutes a non-convex optimization problem. First, the globally optimal solution is obtained by applying the high-complexity outer polyblock approximation (OPA) algorithm. Then, we invoke the reduced-complexity modified weighted minimum mean square error (WMMSE) algorithm for mitigating the deleterious effects of the realistic imperfect CSI. For the subproblem solved by each WMMSE iteration, the beamforming vectors are derived in the closed form relying on the Lagrangian dual decomposition method. Finally, our simulation results show that the modified WMMSE algorithm's performance is comparable to that of the high-complexity OPA algorithm, which outperforms other benchmark algorithms.

In this paper, we consider the sum power minimization problem via jointly optimizing user association, power control, computation capacity allocation, and location planning in a mobile edge computing (MEC) network with multiple unmanned aerial vehicles (UAVs). To solve the nonconvex problem, we propose a low-complexity algorithm with solving three subproblems iteratively. For the user association subproblem, the compressive sensing-based algorithm is accordingly proposed. For the computation capacity allocation subproblem, the optimal solution is obtained in closed form. For the location planning subproblem, the optimal solution is effectively obtained via one-dimensional search method. To obtain a feasible solution for this iterative algorithm, a fuzzy c-means clustering-based algorithm is proposed. The numerical results show that the proposed algorithm achieves better performance than the conventional approaches.

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.

This paper deals with the challenging issue of the unaffordable channel training overhead in the dense cell-free massive multi-input multi-output (MIMO) system when a high number of users are being simultaneously served. By adopting the user-centric cluster method, a dynamic pilot reuse (DPR) scheme is proposed to allow a pair of users to share a single pilot sequence. Specifically, the proposed reuse scheme is achieved with the objective of maximizing the uplink achievable sum-rate subject to users' signal to interference plus noise ratio (SINR) requirements and pilot resources constraints. On this basis, the SINR expression is derived for any user sharing its pilot with another by utilizing both minimum mean squared error (MMSE) detection and channel estimation. A low complexity pilot reuse algorithm is then developed based on the separation distance between users. The iterative grid search (IGS) method is employed to find the threshold that can be utilized in the proposed algorithm to maximize the sum-rate. Finally, simulation results are presented to show the effectiveness of the DPR scheme with the optimized threshold in terms of the uplink achievable sum-rate.

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.

The weighted sum rate maximization problem of ultra-dense cloud radio access networks (C-RANs) is considered, where realistic fronthaul capacity constraints are incorporated. To reduce the training overhead, pilot reuse is adopted and the transmit beamforming is designed to be robust to the channel estimation errors. In contrast to the conventional C-RAN where the remote radio heads (RRHs) coherently transmit their data symbols to the user, we consider their non-coherent transmission, where no strict phase synchronization is required. By exploiting the classic successive interference cancellation technique, we first derive the closed-form expressions of the individual data rates from each serving RRH to the user and the overall data rate for each user that is not related to their decoding order. Then, we adopt the reweighted l1-norm technique to approximate the l0-norm in the fronthaul capacity constraints as the weighted power constraints. A low-complexity algorithm based on a novel sequential convex approximation (SCA) algorithm is developed to solve the resultant optimization problem with convergence guarantee. A beneficial initialization method is proposed to find the initial points of the SCA algorithm. Our simulation results show that in the high fronthaul capacity regime, the coherent transmission is superior to the non-coherent one in terms of its weighted sum rate. However, significant performance gains can be achieved by the non-coherent transmission over the coherent one in the low fronthaul capacity regime, which is the case in ultradense C-RANs, where mmWave fronthaul links with stringent capacity requirements are employed.

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.

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.

This paper considers the unavailability of complete channel state information (CSI) in ultra-dense cloud radio access networks. The user-centric cluster is adopted to reduce the computational complexity, while the incomplete CSI is considered to reduce the heavy channel training overhead, where only large-scale inter-cluster CSI is available. Channel estimation for intra-cluster CSI is also considered, where we formulate a joint pilot allocation and user equipment (UE) selection problem to maximize the number of admitted UEs with fixed number of pilots. A novel pilot allocation algorithm is proposed by considering the multi-UE pilot interference. Then, we consider robust beam-vector optimization problem subject to UEs' data rate requirements and fronthaul capacity constraints, where the channel estimation error and incomplete inter-cluster CSI are considered. The exact data rate is difficult to obtain in closed form, and instead we conservatively replace it with its lower-bound. The resulting problem is non-convex, combinatorial, and even infeasible. A practical algorithm, based on UE selection, successive convex approximation and semi-definite relaxation approach, is proposed to solve this problem with guaranteed convergence. We strictly prove that the semidefinite relaxation is tight with probability 1. Finally, extensive simulation results are presented to show the fast convergence of our proposed algorithm and demonstrate its superiority over the existing algorithms.

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.

This paper deals with the unavailability of full CSI in ultra-dense user-centric TDD C-RAN. To reduce the channel training overhead, we consider the incomplete CSI case, where only large-scale inter-cluster CSI is available. Channel estimation for intra-cluster CSI is also considered, where we formulate a joint pilot allocation and user equipment (UE) selection problem to maximize the number of admitted UEs with fixed number of pilots. A novel pilot allocation algorithm is proposed by considering the multi-UE pilot interference. Then, we consider robust beam-vector optimization problem subject to UEs' data rate requirements and fronthaul capacity constraints, where the channel estimation error and incomplete inter-cluster CSI are considered. Simulation results demonstrate its superiority over the existing algorithms.

As a promising solution to offload cellular traffic, device-to-device (D2D) communication has been adopted to help disseminate contents. In this paper, the D2D offloading utility is maximized by proposing an optimal content pushing strategy based on the user interests and sharing willingness. Specifically, users are classified into groups by their interest probabilities and carry out D2D communications according to their sharing willingness. Although the formulated optimization problem is nonconvex, the optimal solution is obtained in closed-form by applying Karush-Kuhn-Tucker conditions. The theoretical and simulation results show that more contents should be pushed to the user group that is most willing to share, instead of the group that has the largest number of interested users.

In this paper, optimal content caching strategy is proposed to jointly minimize the cell average outage probability and fronthaul usage in cloud radio access network (Cloud-RAN). Closed form expression of the outage probability conditioned on the user's location is presented, and the cell average outage probability is obtained through the composite Simpson's integration. The caching strategy for jointly optimizing the cell average outage probability and fronthaul usage is formulated as a weighted sum minimization problem, which is a nonlinear 0-1 integer NP-hard problem. In order to deal with the NP-hard problem, at first, two particular caching placement schemes are investigated: the most popular content (MPC) caching scheme and the proposed location-based largest content diversity (LB-LCD) caching scheme. Then a genetic algorithm (GA) based approach is proposed. Numerical results show that the performance of the proposed GA-based approach with significantly reduced computational complexity is close to the optimal performance achieved by exhaustive search based caching strategy.

Hardware-efficient low-complexity precoding is very important in the downlink of Massive MIMO systems for mitigating interference and optimizing performance. In this paper, we propose a correlation-driven optimized Taylor expansion (CD-OTE) precoding scheme to simplify linear minimum mean square error (MMSE) precoding. In order to simplify the hardware-expensive matrix inversion involved in the linear MMSE pre-coder, a Taylor expansion with optimized polynomial coefficients and selection of the most relevant correlation coefficients is proposed. We take into consideration the correlation between different users' channels and develop a general design criterion. Both convergence and complexity analyses are carried out. Simulation results show that the proposed CD-OTE precoder is significantly better than previously reported techniques, while requiring a similar cost.

The capacity of a millimetre-wave (mm-Wave) system can be improved by adopting a hybrid digital-to-analog (D-A) precoding system. Therefore, in this paper, an algorithm is proposed to maximise the capacity of the hybrid D-A mm-Wave system. This algorithm is a geometric approach which is based on the principle of Stiefel Manifold optimisation (SMO). Our proposed algorithm is compared with three known hybrid D-A precoding algorithms in the literature. The analytical and simulation results show that the proposed algorithm achieves higher capacity than the existing hybrid D-A precoding algorithms.

Device-to-Device (D2D) communication has been proposed as a promising way to offload traffic from the cellular network. In this paper, with the joint impact of user preference and selfishness, the D2D assisted content dissemination process is investigated in order to maximize the offloading gain via D2D communications. An alternative group pushing optimization (AGPO) algorithm is proposed to solve the formulated nonconvex problem. In addition, for the special case of two groups, the optimal solution is derived in closed-form to help validate the algorithm. Finally, the simulation results show that the AGPO algorithm converges to the global optimum and has a much lower complexity compared to exhaustive search.

In distributed massive multi-input multi-output (DM-MIMO) system, the number of users simultaneously served is greatly restricted if the pilots allocated for users are orthogonal. In this paper, a dynamic pilot reuse strategy within a single cell DM-MIMO system is proposed in order to reduce pilot overhead. The reuse in this strategy is applied so that maximum average sum-rate is satisfied within limited pilot resources. Specifically, two users in different subcells separated by large distance and meeting a specific data rate level can share the same pilot sequence. To perform this strategy, an expression for signal to interference plus noise ratio (SINR) is first derived for any pair of users who uses the same pilot. Based on this expression, an algorithm is proposed to choose which pairs of users are able to use the same pilot by comparing their potential data rates with a certain threshold. Finally, a method is employed to find the near-optimal threshold that can be utilized in the suggested algorithm to produce the maximum average sum-rate. The simulation results demonstrate that the uplink achievable sum-rate for the proposed strategy is higher than the both cases when no pilot reuse or random pilot reuse are considered.

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.

Device-to-device (D2D) assisted offloading heavily depends on the participation of human users. The content preference and sharing willingness of human users are two crucial factors in the D2D assisted offloading. In this paper, with consideration of these two factors, the optimal content pushing strategy is investigated by formulating an optimization problem to maximize the offloading gain measured by the offloaded traffic. Users are placed into groups according to their content preferences and share content with intergroup and intragroup users at different sharing probabilities. Although the optimization problem is nonconvex, the closed-form optimal solution for a special case is obtained, when the sharing probabilities for intergroup and intragroup users are the same. Furthermore, an alternative group optimization (AGO) algorithm is proposed to solve the general case of the optimization problem. Finally, simulation results are provided to demonstrate the offloading performance achieved by the optimal pushing strategy for the special case and AGO algorithm. An interesting conclusion drawn is that the group with the largest number of interested users is not necessarily given the highest pushing probability. It is more important to give high pushing probability to users with high sharing willingness.

This paper provides a unified framework to deal with the challenges arising in dense cloud radio access networks (C-RAN), which include huge power consumption, limited fronthaul capacity, heavy computational complexity, unavailability of full channel state information (CSI), and so on. Specifically, we aim to jointly optimize the remote radio head (RRH) selection, user equipment (UE)-RRH associations and beam-vectors to minimize the total network power consumption (NPC) for dense multi-channel downlink C-RAN with incomplete CSI subject to per-RRH power constraints, each UE's total rate requirement, and fronthaul link capacity constraints. This optimization problem is NP-hard. In addition, due to the incomplete CSI, the exact expression of UEs' rate expression is intractable. We first conservatively replace UEs' rate expression with its lower bound. Then, based on the successive convex approximation technique and the relationship between the data rate and the mean square error, we propose a single-layer iterative algorithm to solve the NPC minimization problem with convergence guarantee. In each iteration of the algorithm, the Lagrange dual decomposition method is used to derive the structure of the optimal beam-vectors, which facilitates the parallel computations at the baseband unit pool. Furthermore, a bisection UE selection algorithm is proposed to guarantee the feasibility of the problem. Simulation results show the benefits of the proposed algorithms and the fact that a limited amount of CSI is sufficient to achieve performance close to that obtained when perfect CSI is possessed.

We jointly select the fronthaul links and optimize the transmit precoding matrices for maximizing the energy efficiency (EE) of a multiuser multiple-input multiple-output-aided distributed antenna system. The fronthaul link's power consumption is taken into consideration, which is assumed to be proportional to the number of active fronthaul links quantified by using indicator functions. Both the rate requirements and the power constraints of the remote access units are considered. Under realistic power constraints, some of the users cannot be admitted. Hence, we formulate a two-stage optimization problem. In Stage I, a novel user selection method is proposed for determining the maximum number of admitted users. In Stage II, we deal with the EE optimization problem. First, the indicator function is approximated by a smooth concave logarithmic function. Second, a triple-layer iterative algorithm is proposed for solving the approximated EE optimization problem, which is proved to converge to the Karush-Kuhn-Tucker conditions of the smoothened EE optimization problem. To further reduce the complexity, a single-layer iterative algorithm is conceived, which guarantees convergence. Our simulation results show that the proposed user selection algorithm approaches the performance of the exhaustive search method. Finally, the proposed algorithms are capable of achieving an order of magnitude higher EE than its conventional counterpart operating without considering link selection.

This paper jointly optimizes the precoding matrices and the set of active remote radio heads (RRHs) to minimize the network power consumption for a user-centric cloud radio access network, where both the RRHs and users have multiple antennas and each user is served by its nearby RRHs. Both users' rate requirements and per-RRH power constraints are considered. Due to these conflicting constraints, this optimization problem may be infeasible. In this paper, we propose to solve this problem in two stages. In Stage I, a low-complexity user selection algorithm is proposed to find the largest subset of feasible users. In Stage II, a low-complexity algorithm is proposed to solve the optimization problem with the users selected from Stage I. Specifically, the re-weighted l1-norm minimization method is used to transform the original problem with non-smooth objective function into a series of weighted power minimization (WPM) problems, each of which can be solved by the weighted minimum mean square error (WMMSE) method. The solution obtained by the WMMSE method is proved to satisfy the Karush-Kuhn-Tucker conditions of the WPM problem. Moreover, a low-complexity algorithm based on Newton's method and the gradient descent method is developed to update the precoder matrices in each iteration of the WMMSE method. Simulation results demonstrate the rapid convergence of the proposed algorithms and the benefits of equipping multiple antennas at the user side. Moreover, the proposed algorithm is shown to achieve near-optimal performance in terms of NPC.

In this paper, we study widely linear precoding techniques to mitigate in-phase/quadrature-phase (IQ) imbalance (IQI) in the downlink of large-scale multiple-input multiple-output (MIMO) systems. We adopt a real-valued signal model, which considers the IQI at the transmitter, and then develop widely linear zero-forcing (WL-ZF), widely linear matched filter, widely linear minimum mean-squared error, and widely linear block-diagonalization (WL-BD) type precoding algorithms for both single- and multiple-antenna users. We also present a performance analysis of WL-ZF and WL-BD. It is proved that without IQI, WL-ZF has exactly the same multiplexing gain and power offset as ZF, while when IQI exists, WL-ZF achieves the same multiplexing gain as ZF with ideal IQ branches, but with a minor power loss, which is related to the system scale and the IQ parameters. We also compare the performance of WL-BD with BD. The analysis shows that with ideal IQ branches, WL-BD has the same data rate as BD, while when IQI exists, WL-BD achieves the same multiplexing gain as BD without IQ imbalance. Numerical results verify the analysis and show that the proposed widely linear type precoding methods significantly outperform their conventional counterparts with IQI and approach those with ideal IQ branches.

Millimetre waves (mm-Waves) with massive multiple input and multiple output (MIMO) have the potential to fulfill fifth generation (5G) traffic demands. In this paper, a hybrid digital-to-analog (D-A) precoding system is investigated and a particle swarm optimisation (PSO) based joint D-A precoding optimisation algorithm is proposed. This algorithm maximises the capacity of the hybrid D-A mm-Wave massive MIMO system. The proposed algorithm is compared with three known hybrid D-A precoding algorithms. The analytical and simulation results show that the proposed algorithm achieves higher capacity than the existing hybrid D-A precoding algorithms.

This paper jointly optimizes the precoding matrices and the set of active remote radio heads (RRHs) to minimize the network power consumption for a cloud radio access network (C-RAN) where both the RRHs and users all have multiple antennas. Both users' rate requirements and per-RRH power constraints are considered. Due to these conflicting constraints, this optimization problem may be infeasible. We propose to solve this problem with two phases. In Phase I, a new approach is proposed to check the feasibility of the original problem. If the feasibility is guaranteed, in Phase II, a low- complexity algorithm is proposed to solve the original optimization problem. Simulation results demonstrate the rapid convergence of the proposed algorithms and the benefits of equipping multiple antennas at the user side.

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.

In this paper, we consider the problem of maximizing the weighted sum energy efficiency (WS-EE) of a multi-input single-output (MISO) ad hoc network. To solve this problem, we develop one low-complexity distributed beamforming algorithm based on pricing mechanism. Specifically, each node updates its price information and broadcasts it to all the other nodes. Having collected all the information, each node selects its beam-vector in closed-form with low computational complexity. The convergence of this algorithm is strictly proved. Compared with the existing two-layer optimization algorithm, our algorithm has lower computational complexity without performance loss. Simulation results show that the proposed algorithm performs slightly worse than the centralized algorithm, but requires much less information exchange overhead.

In this paper, we propose widely-linear blockdiagonalization (BD) type precoding techniques to alleviate the impact of IQ imbalance in the downlink Massive multi-input multi-output (MIMO) systems. We first introduce a real-valued signal model and then develop widely-linear BD (WL-BD) type precoding algorithms, i.e., WL-BD, widely linear regularized BD (WL-RBD) and widely linear simplified generalized MMSE channel inversion (WL-S-GMI). We also present analysis of the sum-rate and multiplexing gain achieved by the proposed WLBD for scenarios with and without IQ imbalance. Numerical results verify the analysis and show that WL-BD type precoding methods significantly outperform their conventional counterparts with IQ imbalance and approach the ideal case.

The joint estimation and compensation of IQ imbalance (IQI) parameters at both transmitter (TX) and receiver (RX) is studied in this paper. We develop a generalized system model with a reduced number of parameters (RNP) that covers a wide range of mobile communications scenarios. We devise efficient direct least-squares (DLS) and alternating least-squares (ALS) techniques for IQI parameter estimation based on the generalized system model. For the ALS based method, we prove that the algorithm will converge to a local optimal solution of the optimization problem. Numerical results show that compared with a previously reported method, the proposed DLS-RNP achieves similar performance with a reduced computational complexity, and the proposed ALS-RNP algorithm has significantly better performance with comparable complexity, with a gain over 5 dB for QPSK and 10 dB for 64QAM in the high signal-to-noise-ratio (SNR) region.

This paper studies the impact of hardware mismatch (11M) between the base station (BS) and the user equipment (UE) in the downlink (DL) of large-scale antenna systems. Analytical expressions to predict the achievable rates are derived for different precoding methods, i.e., matched filter (MF) and regularized zero-forcing (RZF), using large system analysis techniques. Furthermore, the upper bounds on achievable rates of MF and RZF with 11M are investigated, which are related to the statistics of the circuit gains of the mismatched hardware. Moreover, we present a study of 11M calibration, where we take zero-forcing (ZF) precoding as an example to compare two 11M calibration schemes, i.e., Pre-precoding Calibration (Pre-Cal) and Post-precoding Calibration (Post-Cal). The analysis shows that Pre-Cal outperforms Post-Cal schemes. Monte-Carlo simulations are carried out, and numerical results demonstrate the correctness of the analysis.

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.

We consider the problem of maximizing the weighted sum energy efficiency (WS-EE) in ad hoc networks. To solve this problem in a distributed manner, one novel distributed adaptive-pricing algorithm is developed based on limited information exchange among the nodes. Specifically, each node updates its current interference information and broadcasts it to the other nodes. Having collected all this information, each node can adjust its transmit power accordingly with simple arithmetical operations. Then iterate these two steps. This algorithm is strictly proven to be convergent and can attain the KKT optimality conditions of the problem. Moreover, an alternative centralized algorithm based on gradient projection method is proposed to serve as the performance benchmark. Simulation results show that the proposed distributed algorithm converges rapidly. Furthermore, this distributed algorithm performs as well as the centralized one and significantly outperforms the existing algorithm in terms of the WS-EE.

We consider the problem of maximizing the energy efficiency (EE) for a MIMO interference channel (IC), with the power constraint on each link. To obtain totally distributed solutions, this problem is formulated as a noncooperative game. We show that this game always admits a Nash equilibra (NE). Importantly, the sufficient condition that one can check to guarantee the uniqueness of the NE is derived. To reach the NE of this game, we provide a totally distributed EE algorithm, in which each player employs the fractional programming to update his own solution. These updates can be performed in a completely distributed and asynchronous fashion. Sufficient conditions that guarantee the convergence of the algorithm have been given as well. Simulation results show that the proposed algorithm converges fast and significantly outperforms the existing algorithms in terms of the sum-EE or the sum-rate.

This paper studies the impact of hardware mismatch (HM) between base station (BS) and user equipment in the downlink of large scale antenna systems. Analytical expressions to predict the achievable rates are derived for different precoding methods, i.e. matched filter (MF) and regularized zero-forcing (RZF), using large system analysis techniques. Furthermore, the asymptotic downlink signal to interference plus noise ratio (SINR) under HM is investigated, which is only related to the variances of circuit gains in most practical scenarios. Monte-Carlo simulations are carried out, and numerical results demonstrate the correctness of the analysis.

This paper aims to design an optimal set of beam-vectors for multi-band multi-antenna cognitive radio networks that jointly allocate power over both space and frequency, so that the sum power of secondary users (SUs) is minimized, subject to rate demands at the SUs, as well as the interference constraints imposed by primary users. Unlike the rate maximization problems, which are always feasible, this power minimization (PM) problem may be infeasible due to the rate constraints. Therefore, we provide a complete analysis of the PM problem by splitting the solution into two separate phases. In phase I, a novel method is developed to check the feasibility of the PM problem by considering an alternative problem, where one additional variable is introduced. This alternative problem is always feasible and one algorithm based on network duality and geometric programs is developed to solve it. In phase II, a novel algorithm is developed to solve the PM problem. This algorithm can be implemented in an online fashion. Furthermore, this algorithm is proved to converge to a Karush-Kuhn-Tucker point of the PM problem. Simulation results show that the proposed algorithms converge in only a few iterations and significantly outperform the existing single-band method in terms of both the feasibility probabilities and power savings.

In this paper, the downlink signal-to-interference-plus-noise ratio (SINR) distribution in multiuser large scale antenna systems with conjugate beamforming and Rayleigh fading is investigated. The probability density function (PDF) is derived and the distribution in high signal-to-noise ratio (SNR) regime is studied. Results indicate that the PDF of downlink SINR converges to F distribution when the interference is dominant over noise. It is interesting that the asymptotic SINR is just the reciprocal of the ratio of the number of users U to the number of transmit antennas N, and is irrelevant to the average transmit power when N and U grow with fixed ratio. However, when U is a large constant, the transmit power could be proportional to equation to maintain a specified quality of service (QoS), as a result of the large scale antenna system effect. Simulation results validate the derived PDF and analytical results.

In this paper, a novel optimization objective namely weighted sum rate is proposed for beamformer design in single group multicast systems. As an upper bound and special case of maximizing minimum rate, it can overcome previous disadvantages of computation complexity and sensitivity to channel state when the weights are chosen properly. Although the corresponding optimization problem is nonconvex, some properties of the optimal solution and closed-form solutions under some usual special cases are derived. In addition, an iterative algorithm with low computation complexity for general case is proposed. Simulation results show that the beamformer obtained by the iterative algorithm not only improves the minimum rate and average rate, but also gets better fairness and overall performance compared with previous schemes.

The problem of energy-efficient joint beamforming and antenna selection for multicast systems is consider in this paper. Under the performance objective of maximizing the number of bits per joule of energy consumed, beamformer design, optimal transmit power and antenna selection is discussed respectively. The beamforming problem is converted to an equivalent form of the max-min problem and we propose a suboptimal solution with low complexity whose validity is verified by the numerical results. The existence and uniqueness of the optimal transmit power is proved. Then, a simple antenna selection scheme is presented. In the end, we combine the above algorithms into a joint beamforming and antenna selection algorithm. Simulation results show the performance gain of the proposed scheme. A great deal of energy can be saved at the expense of acceptable throughput loss especially with low number of users or short distance. In addition, antenna selection is necessary for energy-efficient design in multi-antenna systems through the performance comparison.