Energy efficiency has become increasingly pivotal for sustainable wireless communications, driving the exploration of innovative technologies to enhance performance while minimizing energy consumption. Movable antenna (MA) technology emerges as a promising paradigm in this pursuit, introducing enhanced spatial degrees of freedom by dynamically adjusting antenna positions at the base station (BS). In this paper, we investigate the energy-efficient design problem for downlink communication systems, where the BS is equipped with MAs and serves multiple single-antenna users.We develop a comprehensive energy efficiency model that integrates the communication sum rate with the power consumption associated with both MA movements and signal transmissions. We aim to maximize the energy efficiency by jointly optimizing the transmit beamforming and antenna positions at the BS, subject to practical constraints including the transmit power budget, minimum inter-antenna distance, and maximum movement range. To address this non-convex problem, we propose an efficient alternating optimization algorithm that iteratively solves the beamforming and MA position optimization subproblems using successive convex approximation and particle swarm optimization methods, respectively. Extensive simulations show that the proposed MA-aided system achieves significantly higher energy efficiency than conventional fixed-position antenna systems and hybrid analog/digital array systems with the same number of radio frequency chains.

Reconfigurable Intelligent Surface (RIS)-based direct modulation communication systems have garnered significant attention due to their low cost, low power consumption, and baseband-less characteristics. However, these systems face challenges such as the random time-varying coding state of the RIS and the difficulty in implementing beamforming in direct modulation. In this paper, we propose a simple and effective joint space-time coding approach for RIS that enables simultaneous realization of both direct modulation communication and beamforming. By modeling the transmitted signals of the RIS using space-time coding, we show that the time coding determines the direct modulation functionality, while the space coding governs the beamforming. Consequently, we introduce a joint time-space coding technique by performing exclusive-or (XOR) operations on the time and space coding sequences, enabling both functionalities to be achieved concurrently. Numerical simulations demonstrate the effectiveness of the proposed method. Furthermore, we design and fabricate a transmissive 1-bit phase reconfigurable RIS operating in the 3.4-3.79 GHz frequency band for the implementation of a direct modulation communication system. Experimental results reveal that the bit error rate (BER) is significantly reduced when joint space-time coding is used, compared to using time coding alone. Additionally, the root-mean-square error vector magnitude (rmsEVM) of the constellation diagram is reduced by 55%. This technique is promising for applications in the Internet of Things (IoT), contributing to the development of intelligent networks for electronic devices.

This letter proposes a distributed path selection algorithm based on the meta multi-agent proximal policy optimization (Meta-MAPPO). The algorithm leverages transferable knowledge to achieve faster and more stable policy optimization in dynamic satellite networks. We integrate meta-learning into the MAPPO framework, equipping agents with rapid adaptation capabilities and enhancing convergence efficiency through experience sharing. Simulation results on a 96-satellite Walker–Delta constellation demonstrate that the proposed framework achieves at least a 5% reduction in average end-to-end delay, maintains zero packet loss, and converges faster, demonstrating its efficiency and robustness in dynamic satellite network environments.

Reliable anti-jamming capability is a critical requirement for Internet of Things (IoT) devices operating in dense and interference-prone wireless environments, where spectrum congestion and co-channel interference can severely degrade communication quality. This paper presents a compact time-modulated phase-center reconfigurable antenna (PCRA) that achieves high-precision null steering while maintaining a small form factor suitable for resource-constrained IoT applications, by employing a periodic time-modulation technique to dynamically switch among multiple radiation states with distinct phase centers but nearly identical amplitude patterns. This mechanism enables angular resolution of 1° in null steering and mitigates the limitations of conventional reconfigurable antennas and beamforming arrays, which often suffer from hardware nonlinearity and restricted discrete states, thereby providing enhanced flexibility in spatial interference control. A prototype was fabricated and experimentally validated under quadrature phase-shift keying (QPSK) modulation, demonstrating an interference rejection ratio exceeding 20 dB, constellation and eye diagrams, together with bit error rate and error vector magnitude measurements, further confirmed the improvement in communication robustness. With its compact footprint, low implementation complexity, and effective anti-jamming capability, the proposed PCRA offers a promising hardware solution for emerging IoT scenarios, including smart homes, UAV-enabled networks, and intelligent transportation systems.

This paper proposes a novel IRS-aided framework for secure sensing, which aims to minimize the worst-case Cram´er-Rao Bound (WC-CRB) within an entire surveillance area by optimizing the IRS reflecting beamforming, enabling reliable and secure localization of arbitrary and unknown targets. Specifically, we first establish a general IRS-aided localization coverage model and derive the closed-form expression for the CRB of an arbitrary point, which reveals the relationship between the localization error bound and the Fisher information of the angle of arrival (AOA), angle of departure (AOD) and delay. To solve this challenging min-max optimization problem, we design efficient algorithms for different area types. For sector area, we first represent the Fisher information as trigonometric polynomials, then construct the WC-CRB coverage constraint as a non-negativity problem of these polynomials, and finally approximate it as an efficiently solvable semidefinite program (SDP). For the more challenging case of arbitrarily shaped area, we propose a two-tiered solution comprising a low-complexity heuristic algorithm based on geometric approximation and a high-performance detailed design that accurately solves the problem by decomposing the irregular boundary into multiple continuous segments. Numerical simulations validate the superiority of the proposed framework, demonstrating that our designs significantly outperform various benchmark schemes in terms of robustness and performance uniformity. The results show that the framework not only effectively reduces the WC-CRB but also achieves a highly uniform performance coverage across the entire area, providing a reliable and efficient solution for practical localization security applications.

Simultaneous wireless information and power transfer (SWIPT) has been envisioned as a promising technology to support ubiquitous connectivity and reliable sustainability in Internet-of-Things (IoT) networks, which, however, generally suffers from severe attenuation caused by long distance propagation, leading to inefficient wireless power transfer (WPT) for energy harvesting receivers (EHRs). This paper proposes to introduce emerging intelligent reflecting surface (IRS) and movable antenna (MA) technologies into SWIPT systems aiming at enhancing information transmission for information decoding receivers (IDRs) and improving receive power of EHRs. We consider to maximize the weighted sum-rate of IDRs via jointly optimizing the active and passive beamforming at the base station (BS) and IRS, respectively, together with the positions of MAs, while guaranteeing the individual requirement of each EHR. To tackle this challenging task due to the non-convexity of associated optimization, we develop an efficient algorithm combining weighted minimal mean square error (WMMSE), block coordinate descent (BCD), majorization-minimization (MM), and penalty duality decomposition (PDD) frameworks. Besides, we present a feasibility characterization method to examine the achievability of EHRs’ requirements. Simulation results demonstrate the significant benefits of our proposed solutions. Particularly, the optimized IRS configuration may exhibit higher performance gain than MA counterpart under our considered scenario.

Stacked intelligent metasurface (SIM) extends the concept of single-layer reconfigurable holographic surfaces (RHS) by incorporating a multi-layered structure, thereby providing enhanced control over electromagnetic wave propagation and improved signal processing capabilities. This study investigates the potential of SIM in enhancing the rate fairness in multiuser downlink systems by addressing two key optimization problems: maximizing the minimum rate (MR) and maximizing the geometric mean of rates (GMR). The former strives to enhance the minimum user rate, thereby ensuring fairness among users, while the latter relaxes fairness requirements to strike a better trade-off between user fairness and system sum-rate (SR). For the MR maximization, we adopt a consensus alternating direction method of multipliers (ADMM)-based approach, which decomposes the approximated problem into sub-problems with closed-form solutions. For GMR maximization, we develop an alternating optimization (AO)-based algorithm that also yields closed-form solutions and can be seamlessly adapted for SR maximization. Numerical results validate the effectiveness and convergence of the proposed algorithms. Comparative evaluations show that MR maximization ensures near-perfect fairness, while GMR maximization balances fairness and system SR. Furthermore, the two proposed algorithms respectively outperform existing related works in terms of MR and SR performance. Lastly, SIM with lower power consumption achieves performance comparable to that of multi-antenna digital beamforming.

Movable antenna (MA) is an emerging technology which can reconfigure wireless channels via adaptive antenna position adjustments at transceivers, thereby bringing additional spatial degrees of freedom for improving system performance. In this paper, from a security perspective, we exploit the MA-enabled legitimate jammer (MAJ) to subvert suspicious multiuser downlink communications consisting of one suspicious transmitter (ST) and multiple suspicious receivers (SRs). Specifically, our objective is to minimize the benefit (the sum rate of all SRs or the minimum rate among all SRs) of such suspicious communications, by jointly optimizing antenna positions and the jamming beamforming at the MAJ. However, the key challenge lies in that given the MAJ’s actions, the ST can reactively adjust its power allocations to instead maximize its benefit for mitigating the unfavorable interference. Such flexible behavior of the ST confuses the optimization design of the MAJ to a certain extent. Facing this difficulty, corresponding to the above two different benefits: i) we respectively determine the optimal behavior of the ST given the MAJ’s actions; ii) armed with these, we arrive at two simplified problems and then develop effective alternating optimization based algorithms to iteratively solve them. In addition to these, we also focus on the special case of two SRs, and reveal insightful conclusions about the deployment rule of antenna positions at the MAJ. Furthermore, we analyze the ideal antenna deployment scheme at the MAJ for achieving the globally performance lower bound. Numerical results demonstrate the effectiveness of our proposed schemes compared to conventional fixed-position antenna (FPA) and other competitive benchmarks.

This paper proposes a novel dual-intelligent reflecting surface (IRS) aided interference-limited simultaneous wireless information and power transfer (SWIPT) system with independent power splitting (PS), where each receiving antenna applies different PS factors to offer an advantageous trade-off between the useful information and harvested energy.We separately establish the near- and hybrid-field channel models for IRS-reflected links to evaluate the performance gain more precisely and practically. Specifically, we formulate an optimization problem of maximizing the harvested power by jointly optimizing dual-IRS phase shifts, independent PS ratio, and receive beamforming vector in both near- and hybrid-field cases. In the near-field case, the alternating optimization algorithm is proposed to solve the non-convex problem by applying the Lagrange duality method and the difference-of-convex (DC) programming. In the hybrid-field case, we first present an interesting result that the AP-IRS-user channel gains are invariant to the phase shifts of dual-IRS, which allows the optimization problem to be transformed into a convex one. Then, we derive the asymptotic performance of the combined channel gains in closed-form and analyze the characteristics of the dual-IRS. Numerical results validate our analysis and indicate the performance gains of the proposed scheme that dual-IRS-aided SWIPT with independent PS over other benchmark schemes.

Symbiotic radio (SR) is a promising technology designed to meet the increasing demand for spectrum-efficient communication. However, the small size of backscatter devices (BDs), which are typically equipped with a single antenna, poses challenges in achieving sufficient diversity or spatial multiplexing, thereby hindering the advancement of SR. To address this issue, we introduce fluid antennas (FAs) into SR, enabling devices to dynamically adjust their positions to create a favorable wireless environment and overcome spatial constraints, thereby achieving significant diversity gains. In this paper, we investigate the uplink performance of FA-assisted SR (FA-SR). First, we propose a novel collaborative cancellation channel estimation scheme based on least squares regression (CC-LSR) for scenarios with imperfect channel state information (CSI). We then derive tight lower bound expressions for the channel capacity under both perfect and imperfect CSI cases and formulate the corresponding weighted sum channel capacity (WSCC) optimization problems. The positions of the FAs and the combining vectors are jointly optimized to maximize the lower bound of the WSCC. To solve these problems, we develop joint optimization methods for both perfect and imperfect CSI scenarios using chaotic sequence-based adaptive particle swarm optimization (CSA-PSO). Nevertheless, the high computational complexity of joint optimization poses challenges for practical implementation. To this end, we propose a progressive optimization framework (POF) tailored to both perfect and imperfect CSI scenarios, in which the original problem is divided into three subproblems that are progressively solved to find locally optimal solutions. Numerical results demonstrate that POF significantly reduces computational complexity with minimal performance loss compared to joint optimization methods, particularly under imperfect CSI conditions.

While low-altitude wireless networks (LAWNs) based on uncrewed aerial vehicles (UAVs) offer high mobility, flexibility, and coverage for urban communications, they face severe signal attenuation in dense environments due to obstructions. To address this critical issue, we consider introducing collaborative beamforming (CB) of UAVs and omnidirectional reconfigurable beamforming (ORB) of simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) to enhance the signal quality and directionality. On this basis, we formulate a joint rate and energy optimization problem (JREOP) to maximize the transmission rate of the overall system, while minimizing the energy consumption of the UAV swarm. Due to the non-convex and NP-hard nature of JREOP, we propose a heterogeneous multi-agent collaborative dynamic (HMCD) optimization framework, which has two core components. The first component is a simulated annealing (SA)-based STAR-RIS control method, which dynamically optimizes reflection and transmission coefficients to enhance signal propagation. The second component is an improved multi-agent deep reinforcement learning (MADRL) control method, which incorporates a self-attention evaluation mechanism to capture interactions between UAVs and an adaptive velocity transition mechanism to enhance training stability. Simulation results demonstrate that HMCD outperforms various baselines in terms of convergence speed, average transmission rate, and energy consumption. Further analysis reveals that the average transmission rate of the overall system scales positively with both UAV count and STAR-RIS element numbers.

In intelligent reflecting surface (IRS)-assisted multiple-input multiple-output (MIMO) systems, a strong line-of-sight (LoS) link is required to compensate for the severe cascaded path loss. However, such a link renders the effective channel highly rank-deficient and fundamentally limits spatial multiplexing. To overcome this limitation, this paper leverages the large aperture of sparse arrays to harness near-field spherical wavefronts, and establishes a deterministic deployment criterion that strategically positions the IRS in the near-field of a base station (BS). This placement exploits the spherical wavefronts of the BS–IRS link to engineer decorrelated channels, thereby fundamentally overcoming the rank-deficiency issue in far-field cascaded channels. Based on a physical channel model for the sparse BS array and the IRS, we characterize the rank properties and inter-user correlation of the cascaded BS–IRS–user channel. We further derive a closed-form favorable propagation metric that reveals how the sparse array geometry and the IRS position can be tuned to reduce inter-user channel correlation. The resulting geometry-driven deployment rule provides a simple guideline for creating a favorable propagation environment with enhanced effective degrees of freedom. The favorable channel statistics induced by our deployment criterion enable a low-complexity maximum-ratio transmission (MRT) precoding scheme. This serves as the foundation for an efficient algorithm that jointly optimizes the IRS phase shifts and power allocation based solely on long-term statistical channel state information (CSI). Simulation results validate the effectiveness of our deployment criterion and demonstrate that our optimization framework achieves significant performance gains over benchmark schemes.

To meet the emerging demands for rapid data aggregation and reliable information transmission in future wireless applications, a novel system architecture integrating Over-the-Air Computation (AirComp) and downlink multi-user communication via a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is proposed in this paper. In the considered cellular scenario, an unmanned aerial vehicle (UAV) carries a STAR-RIS beneath its fuselage, creating a programmable aerial platform that concurrently serves Internet-of-Things (IoT) devices and conventional mobile users. The STAR-RIS operates in transmission mode to enable efficient wireless data aggregation of IoT devices, while its reflection mode establishes high-quality downlink channels from the base station (BS) to multiple users. Capturing the true electromagnetic behavior of the STAR-RIS, we explicitly model the practical coupling between the reflection and transmission phase shifts. Two optimization problems are then formulated: one minimizes AirComp distortion and the other maximizes the minimum user rate in the downlink. Both non-convex problems are tackled by efficient iterative algorithms derived from the penalty dual decomposition (PDD) framework. Extensive simulations confirm that the proposed design markedly outperforms baseline approaches and its performance can approach that of ideal phase-shift control by enhancing the key system parameters. Additionally, the trade-off between computation and communication performance is demonstrated.

This paper investigates an intelligent reflecting surface (IRS) aided wireless federated learning (FL) system, where an access point (AP) coordinates multiple edge devices to train a machine leaning model without sharing their own raw data. During the training process, we exploit the joint channel recon figuration via IRS and resource allocation design to reduce the latency of a FL task. Particularly, we propose three transmission protocols for assisting the local model uploading from multiple devices to an AP, namely IRS aided time division multiple access (I-TDMA), IRS aided frequency division multiple access (I-FDMA), and IRS aided non-orthogonal multiple access (I NOMA), to investigate the impact of IRS on the multiple access for FL. Under the three protocols, we minimize the per-round latency subject to a given training loss by jointly optimizing the device scheduling, IRS phase-shifts, and communication computation resource allocation. For the associated problem under I-TDMA, an efficient algorithm is proposed to solve it optimally by exploiting its intrinsic structure, whereas the high quality solutions of the problems under I-FDMA and I-NOMA are obtained by invoking a successive convex approximation (SCA) based approach. Then, we further develop a theoretical framework for the performance comparison of the proposed three transmission protocols. Sufficient conditions for ensuring that I-TDMA outperforms I-NOMA and those of its opposite are unveiled, which is fundamentally different from that NOMA always outperforms TDMA in the system without IRS. Simulation results validate our theoretical findings and also demonstrate the usefulness of IRS for enhancing the fundamental tradeoff between the learning latency and learning accuracy.

The rapid development of the low-altitude economy (LAE) has significantly increased the utilization of autonomous aerial vehicles (AAVs) in various applications, necessitating efficient and secure communication methods among AAV swarms. In this work, we aim to introduce distributed collaborative beamforming (DCB) into AAV swarms and handle the eavesdropper collusion by controlling the corresponding signal distributions. Specifically, we consider a two-way DCB-enabled aerial communication between two AAV swarms and construct these swarms as two AAV virtual antenna arrays. Then, we minimize the two-way known secrecy capacity and maximum sidelobe level to avoid information leakage from the known and unknown eavesdroppers, respectively. Simultaneously, we also minimize the energy consumption of AAVs when constructing virtual antenna arrays. Due to the conflicting relationships between secure performance and energy efficiency, we consider these objectives by formulating a multi-objective optimization problem, which is NP-hard and with a large number of decision variables. Accordingly, we design a novel generative swarm intelligence (GenSI) framework to solve the problem with less overhead, which contains a conditional variational autoencoder (CVAE)-based generative method and a proposed powerful swarm intelligence algorithm. In this framework, CVAE can collect expert solutions obtained by the swarm intelligence algorithm in other environment states to explore characteristics and patterns, thereby directly generating high-quality initial solutions in new environment factors for the swarm intelligence algorithm to search solution space efficiently. Simulation results show that the proposed swarm intelligence algorithm outperforms other state-of-the-art baseline algorithms, and the GenSI can achieve similar optimization results by using far fewer iterations than the ordinary swarm intelligence algorithm. Experimental tests demonstrate that introducing the CVAE mechanism achieves a 58.7% reduction in execution time, which enables the deployment of GenSI even on AAV platforms with limited computing power.

This paper investigates detection orientated integrated sensing and communication (ISAC) system aided by hybrid reconfigurable intelligent surface (RIS). Target detection is conducted through communication signal echoes under the practical condition of unknown attenuation coefficient and sensing noise covariance, which makes our study more challenging than existing pertinent works. Firstly, we develop a closedform based generalized likelihood ratio test (GLRT) detector, which first effectively extrapolates unknown parameters through maximum likelihood estimation and then conducts hypothesis testing. Besides, we derive the asymptotic detection probability of the proposed GLRT detector in an analytic form, which is highly accurate for moderate sample size. Based on the above analysis, we propose robust beamforming design to maximize the worst-case detection probability while ensuring ergodic communication rate in awareness of channel state information (CSI) uncertainties. We provide a semidefinite programming (SDP) formulation to solve the robust beamforming problem. Additionally, by converting the variational and ergodic forms in robust formulation into explicit approximations, we further develop an efficient second order cone programming (SOCP) based solution, which is highly reliable when the CSI uncertainty becomes low. Numerical results validate the efficacy of the proposed GLRT detector, the correctness of the detection performance analysis, and the benefit of robust beamforming against CSI uncertainty.

This paper investigates a multi-intelligent reflecting surface (IRS) aided integrated sensing and communication (ISAC) system, where multiple IRSs are strategically deployed not only to assist the communication from a multi-antenna base station (BS) to a multi-antenna communication user (CU), but also enable the sensing service for a point target in the non-line-of-sight (NLoS) region of the BS. First, we propose a hybrid multi-IRS architecture, which consists of several passive IRSs and one semi-passive IRS equipped with both active sensors and reflecting elements. To be specific, the active sensors are exploited to receive the echo signals for estimating the target’s angle information, and the multiple reflecting paths provided by multi-IRS are employed to improve the degree of freedoms (DoFs) of communication. Under the given budget on the number of total IRSs elements, we theoretically show that increasing the number of deployed IRSs is beneficial for improving DoFs of spatial multiplexing for communication while increasing the Crámer-Rao bound (CRB) of target estimation, which unveils a fundamental tradeoff between the sensing and communication performance. To characterize the rate-CRB tradeoff, we study a rate maximization problem, by optimizing the BS transmit covariance matrix, IRSs phase-shifts, and the number of deployed IRSs, subject to a maximum CRB constraint. Analytical results reveal that the communication-oriented design becomes optimal when the total number of IRSs elements exceeds a certain threshold, wherein the relationships of the rate and CRB with the number of IRS elements/sensors, transmit power, and the number of deployed IRSs are theoretically derived and demystified. Simulation results validate our theoretical findings and also demonstrate the superiority of our proposed designs over the benchmark schemes.

core challenge in physical-layer security is the difficulty of obtaining the channel state information (CSI) of potential eavesdroppers. The inherent sensing functionality of integrated sensing and communication (ISAC) systems offers a promising solution by enabling the estimation of key parameters, such as the eavesdropper’s angles of departure (AoDs). Capitalizing on this capability, we propose a sensing-assisted secure communication scheme for a movable antenna (MA)-aided ISAC system. The scheme comprises two stages: eavesdropper AoD sensing and secure communication. In the first stage, the base station (BS) optimizes the positions of its transmit and receive MAs to enhance sensing accuracy. We derive the closed-form Cramèr-Rao bound (CRB) for the estimated AoDs to fundamentally characterize how MA positions influence the estimation uncertainty. In the second stage, the BS ensures secure communication by designing a robust beamforming vector that accounts for the AoD uncertainty region and by further optimizing the transmit MAs’ positions to maximize the secrecy rate. To manage the end-to-end design, we formulate a joint optimization problem. This intractable non-convex problem is decomposed into two subproblems. For the first subproblem, we develop an alternating optimization (AO) algorithm to solve the CRB minimization problem. For the second subproblem, we solve the worst-case secrecy rate maximization problem using a method based on backward induction, convex hull construction, and AO. Finally, simulation results are provided to demonstrate the significant advantages of the proposed scheme compared to various benchmarks.

Movable antenna (MA)-enabled multiple-input multiple-output (MIMO) systems offer a promising enhancement for integrated sensing and communication (ISAC) applications. Unlike conventional MIMO systems with fixed-position antenna (FPA) arrays, MAs can flexibly adjust their positions within a given region, enabling reconfiguration of both communication and sensing channels with additional spatial degrees of freedom. In this paper, we propose a unified mutual information (MI) framework for MA-enabled MIMO ISAC systems, where MI characterizes communication performance as reliably conveyable information and sensing performance as extractable target information in cluttered environments. We formulate an optimization problem to maximize the weighted sum of communication and sensing MI by jointly optimizing the transmit beamforming matrix under a transmit power constraint and the MA positions under practical constraints, with a weighting coefficient characterizing their trade-off. To tackle the non-convexity arising from the log-det objective, position constraints, and the nonlinear coupling between optimization variables, we develop an alternating optimization-based algorithm that iteratively updates the transmit beamforming matrix and the MA positions. Specifically, with the fixed MA positions, we optimize the beamforming by approximating the objective function using weighted mean square error and majorization-minimization methods, yielding a closed-form solution. Moreover, with fixed beamforming, the MA positions are sequentially refined by decomposing the position optimization into simpler subproblems, resulting in an efficient suboptimal solution. Numerical results show that the unified MI framework with MAs significantly outperforms conventional FPA systems in both communication and sensing. Channel amplitude heatmap visualizations further illustrate how MA positioning strategies exploit spatial flexibility in array geometry to enhance overall system performance.

This paper investigates a movable antenna (MA)-enabled wireless-powered communication network (WPCN), where multiple wireless devices (WDs) first harvest energy from the downlink signal broadcast by a hybrid access point (HAP) and then transmit information in the uplink using non-orthogonal multiple access. Unlike conventional WPCNs with fixed-position antennas (FPAs), this MA-enabled WPCN allows the MAs at the HAP and the WDs to adjust their positions twice: once before downlink wireless power transfer and once before uplink wireless information transmission. Our goal is to maximize the system sum throughput by jointly optimizing the MA positions, the time allocation, and the uplink power allocation. Considering the characteristics of antenna movement, we explore both continuous and discrete positioning designs, which, after formulation, are found to be non-convex optimization problems. Before tackling these problems, we rigorously prove that using identical MA positions for both downlink and uplink is the optimal strategy in both scenarios, thereby greatly simplifying the problems and enabling easier practical implementation of the system. We then propose alternating optimization-based algorithms to obtain suboptimal solutions for the resulting simplified problems. Simulation results show that: 1) the proposed continuous MA scheme can enhance the sum throughput by up to 395.71% compared to the benchmark with FPAs, even when additional compensation transmission time is provided to the latter; 2) a step size of one-quarter wavelength for the MA motion driver is generally sufficient for the proposed discrete MA scheme to achieve over 80% of the sum throughput performance of the continuous MA scheme; 3) when each moving region is large enough to include multiple optimal positions for the continuous MA scheme, the discrete MA scheme can achieve comparable sum throughput without requiring an excessively small step size.

This paper investigates an intelligent reflecting surface (IRS)-aided movable antenna (MA) system, where multiple IRSs cooperate with a multi-MA base station to extend wireless coverage to multiple target areas. The objective is to maximize the worst-case signal-to-noise ratio (SNR) across all locations within these areas through joint optimization of MA positions, IRS phase shifts, and transmit beamforming. To achieve this while balancing the performance-cost trade-off, we propose three coverage-enhancement schemes: thearea-adaptive MA-IRSscheme, where both the MA positions and IRS phase shifts are adaptively adjusted for each target area; thearea-adaptive MA-staIRSscheme, where only the MA positions are adjusted, while the IRS phase shifts remain unchanged after initial configuration (withstaIRSdenoting static IRSs); and theshared MA-staIRSscheme, where a common MA placement and static IRS configuration are applied across all areas. These schemes lead to challenging non-convex optimization problems with implicit objective functions, which are difficult to solve optimally. To address these problems, we propose a general algorithmic framework that can be applied to solve each problem efficiently albeit suboptimally. Simulation results demonstrate that: 1) the proposed MA-based schemes consistently outperform their fixed-position antenna (FPA)-based counterparts under both area-adaptive and static IRS configurations, with the area-adaptive MA-IRS scheme achieving the highest worst-case SNR; 2) as transmit antennas are typically far fewer than IRS elements, the area-adaptive MA-staIRS scheme may underperform the baseline FPA scheme with area-adaptive IRSs in terms of the worst-case SNR, but a modest increase in antenna number can reverse this trend; 3) under a fixed total cost, the optimal MA-to-IRS-element ratio for the worst-case SNR maximization is empirically found to be proportional to the reciprocal of their unit cost ratio.

Intelligent reflecting surface (IRS) and movable antenna (MA) technologies have been proposed to enhance wireless communications by creating favorable channel conditions. This paper investigates the joint beamforming and antenna position optimization for MA-enabled IRS (MA-IRS)-aided multi-user multiple-input single-output (MU-MISO) communication systems, where the MA-IRS is deployed to aid the communication between the MA-enabled base station (BS) and user equipment (UE). In contrast to conventional fixed position antenna (FPA)-enabled IRS (FPA-IRS), the positions of the reflecting elements of the MA-IRS can be controlled to enhances the wireless channel. To verify the system’s effectiveness and optimize its performance, we formulate a sum-rate maximization problem with a minimum rate threshold constraint for the MU-MISO communication. To tackle the non-convex problem, a product Riemannian manifold optimization (PRMO) method is proposed for the joint optimization of the beamforming and MA positions. Specifically, a product Riemannian manifold space (PRMS) is constructed and the corresponding Riemannian gradient is derived for updating the variables, and the Riemannian exact penalty (REP) method and a Riemannian Broyden-Fletcher-Goldfarb-Shanno (RBFGS) algorithm is exploited to obtain a feasible solution over the PRMS. Simulation results demonstrate that compared with the conventional FPA-IRS-aided communications, the reflecting elements of the MA-IRS can move to the positions with higher channel gain, thus enhancing the system performance. Furthermore, it is shown that optimizing the positions of the reflecting elements brings higher performance gain than controlling the phase shifts of the IRS, and integrating MA with IRS leads to higher performance gains compared to integrating MA with BS.

Driven by intelligent reflecting surface (IRS) and movable antenna (MA) technologies, movable IRS (MIRS) has been proposed to improve the adaptability and performance of conventional IRS, enabling flexible adjustment of the IRS reflecting element positions. This paper investigates MIRS-aided integrated sensing and communication (ISAC) systems. The objective is to minimize the power required for satisfying the quality-of-service (QoS) of sensing and communication by jointly optimizing the MIRS element positions, IRS reflection coefficients, transmit beamforming, and receive filters. To balance the performance-cost trade-off, we proposed two MIRS schemes: element-wise control and array-wise control, where the positions of individual reflecting elements and arrays consisting of multiple elements are controllable, respectively. To address the joint beamforming and position optimization, a product Riemannian manifold optimization (PRMO) method is proposed, where the variables are updated over a constructed product Riemannian manifold space (PRMS) in parallel via penalty-based transformation and Riemannian Broyden–Fletcher–Goldfarb–Shanno (RBFGS) algorithm. Simulation results demonstrate that the proposed MIRS outperforms conventional IRS in power minimization with both element-wise control and array-wise control. Specifically, with different system parameters, the minimum power is achieved by the MIRS with the element-wise control scheme, while suboptimal solution and higher computational efficiency are achieved by the MIRS with array-wise control scheme.

Integrated sensing and communication (ISAC) is a key technology for future 6G networks. Most existing studies focus on monostatic and/or bistatic setups with limited coverage and capabilities. Networked ISAC systems with distributed base stations (BSs) can overcome these limitations. Moreover, movable antenna (MA) architectures offer improved ISAC performance over fixed-position antennas (FPAs) by enabling adaptable antenna movement. In this paper, we utilize the MA to promote communication capability with guaranteed sensing performance via jointly designing beamforming, power allocation, receiving filters and position configuration of transmit/receive MA towards maximizing the sum rate for both downlink (DL) and uplink (UL) users. The optimization problem is highly difficult due to the unique channel model derived from the position coefficient of the MA. To resolve this challenge, via leveraging the cutting-the-edge majorization-minimization (MM) method, we develop an efficient solution that optimizes all variables via convex optimization techniques. Extensive simulation results verify the effectiveness of our proposed algorithms and demonstrate the substantial performance promotion by deploying the MA framework in the networked ISAC system.

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

In this paper, we propose a full-duplex integrated sensing and communication (ISAC) system enabled by a movable antenna (MA). By leveraging the characteristic of MA that can increase the spatial diversity gain, the performance of the system can be enhanced. We formulate a problem of minimizing the total transmit power consumption via jointly optimizing the discrete position of MA elements, beamforming vectors, sensing signal covariance matrix and user transmit power. Given the significant coupling of optimization variables, the formulated problem presents a non-convex optimization challenge that poses difficulties for direct resolution. To address this challenging issue, the discrete binary particle swarm optimization (BPSO) algorithm framework is employed to solve the formulated problem. Specifically, the discrete positions of MA elements are first obtained by iteratively solving the fitness function. The difference-of-convex (DC) programming and successive convex approximation (SCA) are used to handle non-convex and rank-1 terms in the fitness function. Once the BPSO iteration is complete, the discrete positions of MA elements can be determined, and we can obtain the solutions for beamforming vectors, sensing signal covariance matrix and user transmit power. Numerical results demonstrate the superiority of the proposed system in reducing the total transmit power consumption compared with fixed antenna arrays.

In our original paper, we omitted a key step involving the transformation of variableR̃ISACk,j[n]. In this work, we recognize that our initial conclusion, stating that "Hk,jis a negative definite matrix in the feasible region" requires additional clarification and adjustments. To ensure the correctness of the work, we provide the necessary modifications and detailed discussions in this revised version.

In this paper, we investigate the uplink joint signal detection for low-altitude aerial cell-free (CF) networks, where each flying access point (AP) locally processes the received signals and then forwards these information to a central processing unit (CPU) for the final detection. However, unlike terrestrial CF networks that typically adopt optic fiber fronthaul links, wireless fronthaul in aerial CF networks connecting flying APs with the CPU will significantly affect the communication performance, due to the practically limited fronthaul capacity. Therefore, we adopt a realistic channel model for wireless fronthaul links, which experience Rician fading and are shared among the flying APs through a combination of frequency division multiple access (FDMA) and space division multiple access (SDMA) protocol. Taking into account the imperfect and capacity-limited wireless fronthaul, we propose a joint uplink signal detection framework, where the local processing matrix at APs and the central detector at the CPU are designed based on the long-term statistical channel state information (CSI) by leveraging the operator-valued free probability theory. This approach significantly reduces the need for frequent, high-capacity signaling exchanges between APs and the CPU. Numerical results demonstrate the accuracy and effectiveness of the proposed joint signal detection framework.

Conventional antenna arrays rely primarily on digital beamforming for spatial control. While adding more elements can narrow beamwidth and suppress interference, such scaling incurs prohibitive hardware and power costs. Rotatable antennas (RAs), which allow mechanical or electronic adjustment of element orientations, introduce a new degree of freedom to exploit spatial flexibility without enlarging the array. By dynamically optimizing orientations, RAs can substantially improve desired link alignment and interference suppression. This paper investigates RA-enabled multiple-input single-output (MISO) interference channels under co-channel spectrum sharing and formulates a weighted sum-rate maximization problem that jointly optimizes transmit beamforming and antenna orientations. To tackle this nonconvex problem, we develop an alternating optimization (AO) framework that integrates weighted minimum mean-square error (WMMSE)-based beamforming with Frank-Wolfe-based orientation updates. To reduce complexity, we further study orientation optimization under maximum-ratio transmission (MRT) and zero-forcing (ZF) beamforming schemes. For finite-resolution actuators, we construct spherical Fibonacci codebooks and design a cross-entropy method (CEM)-based algorithm for discrete orientation selection. Simulations show that integrating RAs with conventional beamforming markedly increases weighted sum-rate, with gains rising with element directivity. Under discrete orientation control, the proposed CEM algorithm consistently outperforms the nearest-projection baseline.

In this paper, we model the minimum achievable throughput within a transmission block of restricted duration and aim to maximize it in movable antenna (MA)-enabled multiuser downlink communications. Particularly, we account for the antenna movement delay caused by mechanical movement, which has not been fully considered in previous studies, and reveal the trade-off between the delay and signal-to-interference-plus-noise ratio at users. To this end, we first consider a single-user setup to analyze the necessity of antenna movement. By quantizing the virtual angles of arrival, we derive the requisite region size for antenna moving, design the initial MA position, and elucidate the relationship between quantization resolution and moving region size. Furthermore, an efficient algorithm is developed to optimize MA position via successive convex approximation, which is subsequently extended to the general multiuser setup. Numerical results demonstrate that the proposed algorithms outperform fixed-position antenna schemes and existing ones without consideration of movement delay. Additionally, our algorithms exhibit excellent adaptability and stability across various transmission block durations and moving region sizes, and are robust to different antenna moving speeds. This allows the hardware cost of MA-aided systems to be reduced by employing low rotational speed motors.

Terahertz (THz) simultaneous wireless information and power transfer (SWIPT) is a promising technology for enabling ultra-high-rate and low-latency communications in massive battery-free Internet of Things (IoT) deployments for 6G networks. However, conventional THz systems rely on narrow directional beams that necessitate precise alignment, typically achieved through high-overhead beam scanning procedures, which fundamentally at odds with the energy constraints of battery-free IoT devices. In this paper, we propose a novel self-alignment architecture for THz SWIPT leveraging a reconfigurable intelligent surface (RIS) to eliminate complex beam scanning. By integrating phase conjugate circuits at both the base station and user equipment, the RIS facilitates a resonance-based bidirectional retroreflection mechanism, enabling the system to autonomously converge to an aligned state without manual intervention. We develop an analytical channel transfer model and a power cycle model to characterize the resonance-assisted beam alignment process and power transfer efficiency. Simulation results demonstrate that the RIS-enabled system achieves effective spatial power concentration with significant sidelobe suppression, leading to a communication capacity of 127.84 Gbit/s and a received power of 13.62 mW over a 2.2-meter link.

This paper proposes a novel multiple intelligent reflecting surfaces (IRSs) collaborative hybrid localization system, which involves deploying multiple IRSs near the target area and achieving target localization through joint time delay and angle estimation. Specifically, echo signals from all reflective elements are received by each sensor and jointly processed to estimate the time delay and angle parameters. Based on the above model, we derive the Fisher Information Matrix (FIM) for cascaded delay, Angle of Arrival (AOA), and Angle of Departure (AOD) estimation in semi passive passive models, along with the corresponding Cramer Rao Bound (CRB). To achieve precise estimation close to the CRB, we design efficient algorithms for angle and location estimation. For angle estimation, reflective signals are categorized into three cases based on their rank, with different signal preprocessing. By constructing an atomic norm set and minimizing the atomic norm, the joint angle estimation problem is transformed into a convex optimization problem, and low-complexity estimation of multiple AOA and AOD pairs is achieved using the Alternating Direction Method of Multipliers (ADMM). For location estimation, we propose a three-stage localization algorithm that combines weighted least squares, total least squares, and quadratic correction to handle errors in the coefficient matrix and observation vector, thus improving accuracy. Numerical simulations validate the superiority of the proposed system, demonstrating that the system's collaboration, hybrid localization, and distributed deployment provide substantial benefits, as well as the accuracy of the proposed estimation algorithms, particularly in low signal to noise ratio (SNR) condition.

In this paper, we design a novel two-stage transmission framework in millimeter wave-ISAC systems with multiple communication users (CUs) and multiple target scenarios. In stage I, the dual-functional base station (DFBS) performs beam scanning with pilot signals, estimating target direction of arrival angles (DoAs) through the maximum likelihood estimation and multiple signal classification techniques, while the CU estimate DoAs via minimum mean square error and MUSIC techniques. Further, we derive the closed-form Cramér-Rao Bound (CRB) expressions for estimated CU/target DoAs and establish the relationship between channel station information (CSI) error and CRB. In stage II, the DFBS transmits ISAC signals and maximizes the minimum effective signal-to-interference-plus-noise ratio (SINR) of CU by jointly optimizing two stage resources, while meeting sensing performance requirements and accounting for the impact of imperfect CSI. Since the complex interactions and strong coupling among variables, the formulated problem is non-convex and difficult to be solved directly. To address this issue, we begin by employing one-dimensional search to determine the sensing duration of Stage I. Then, based on this result, the DFBS beamforming optimization design is carried out with S-procedure method, penalty-based and successive convex approximation algorithms to convert the original problem into a tractable convex optimization problem. Finally, simulations are executed to confirm the advantages and effectiveness of our developed scheme.

This paper considers an active reconfigurable intelligent surface (RIS) aided device-based uplink integrated sensing and communication (ISAC) system. In this context, base station (BS) receives pilot and communication signals transmitted concurrently from mobile users to provision sensing and communication services. For the considered setup, we investigate beamforming design by jointly optimizing RIS configuration, mobile users’ transmit power and linear combiner at the BS to minimize Cramér-Rao bound (CRB) of angle-of-arrival (AoA) estimation for the sensing users while ensuring spectral efficiency of communication users. The considered device-based sensing paradigm raises unique challenge since communication signals contribute to noise covariance in AoA measurements, which leads to a highly complicated CRB expression. To resolve this challenge, we transfer the problem into a quartic form, equivalently represent covariance matrix inverse into an equation condition, decouple the intractable covariance equality constraint by introducing splitting variables followed by penalty dual-decomposition (PDD) methodology, which develops an iterative process updating all variable blocks alternatively. Extensive numerical results verify the effectiveness of our proposed algorithm and demonstrate the significant advantage of device-based sensing scheme over the device-free counterpart when the sensing targets can get connected in the ISAC network.

Reconfigurable intelligent surfaces (RISs) enhance wireless systems by reshaping propagation environments. However, dynamic metasurfaces (MSs) with numerous phase-shift elements may incur undesired hardware costs and control overhead. In contrast, static MSs (SMSs), configured with static phase shifts that are pre-designed for specific communication demands, offer a cost-effective alternative by eliminating electronic element-wise tuning. Nevertheless, SMSs typically support only a single beam pattern, limiting flexibility in dynamic and multi-user scenarios. In this paper, we propose a novel Movable Intelligent Surface (MIS) technology that enables dynamic beamforming while maintaining static phase shifts. Specifically, we design a MIS architecture comprising two closely stacked transmissive MSs: a larger fixed-position MS 1 and a smaller movable MS 2. By differentially shifting MS 2’s position relative to MS 1, the MIS synthesizes distinct desired beam patterns, overcoming the SMSs’ single-pattern limitation. Then, we model the interaction between MS 2 and MS 1 using binary selection matrices and padding vectors, which allow us to formulate a new optimization problem that jointly designs the MIS phase shifts and selects shifting positions for worst-case signal-to-noise ratio (SNR) maximization. This position selection, equal to beam pattern scheduling, offers a new degree of freedom for RIS-aided systems. To solve the intractable problem, we develop an efficient algorithm that handles unit-modulus and binary constraints and employs manifold optimization methods. Finally, extensive validation results are provided, including both experimental and numerical analysis. We first implement a MIS prototype and perform proof-of-concept experiments, demonstrating the MIS’s ability to synthesize desired beam patterns that achieve beam steering. Numerical results further validate our theoretical modeling and the proposed algorithm. Encouragingly, by introducing a movable MS 2 with a few elements, MIS effectively offers beamforming flexibility for significantly improved performance compared to SMSs. We also draw insights into the optimal MIS configuration and element allocation strategy.

This paper investigates hybrid reconfigurable intelligent surface (RIS) aided integrated sensing and communication (ISAC) scenario that utilizes the echoes of communication signals to accomplish target detection without prior knowledge on signal attenuation coefficient and sensing noise covariance. To realize effective detection, we develop an analytic based generalized likelihood ratio test (GLRT) detector and theoretically analyze its detection performance. Based on that, we further develop an efficient iterative optimization process to conduct robust beamforming design that improves detection performance against channel state information (CSI) uncertainty while guaranteeing achievable ergodic communication rates. Numerical results verify the effectiveness of our proposed GLRT detector, the correctness of our performance analysis, and the benefit of the developed robust beamforming design.

Rate splitting multiple access (RSMA) is regarded as a crucial and powerful physical layer (PHY) paradigm for nextgeneration communication systems. Particularly, users employ successive interference cancellation (SIC) to decode part of the interference while treating the remainder as noise. However, conventional RSMA systems rely on fixed-position antenna arrays, limiting their ability to fully exploit spatial diversity. This constraint reduces beamforming gain and significantly impairs RSMA performance. To address this problem, we propose a movable antenna (MA)-aided RSMA scheme that allows the antennas at the base station (BS) to dynamically adjust their positions. Our objective is to maximize the system sum rate of common and private messages by jointly optimizing the MA positions, beamforming matrix, and common rate allocation. To tackle the formulated non-convex problem, we apply fractional programming (FP) and develop an efficient two-stage, coarse-to-fine-grained searching (CFGS) algorithm to obtain high-quality solutions. Numerical results demonstrate that, with optimized antenna adjustments, the MA-enabled system achieves substantial performance and reliability improvements in RSMA over fixedposition antenna setups.

Integrated Sensing and Communication (ISAC) technology facilitates simultaneous reliable communication and high-precision sensing performance in vehicular networks. Many existing ISAC models treat vehicles as point-like objects, which oversimplifies real-world scenarios. In practice, vehicles have complex shapes and sizes, which may occupy multiple range and angle grids. To address these challenges, we propose an intelligent omni-surface (IOS) mounted on the top surface of an extended vehicle and introduce a novel IOS-aided extended vehicle tracking scheme. Aiming to minimize the Cramér-Rao bound (CRB) for estimating vehicle's angle, distance and velocity while meeting communication rate requirements, we propose a zeroth-order optimization based increasing penalty dual decomposition (ZO-IPDD) algorithm. Additionally, a dimension reduction strategy is employed to mitigate the high computational complexity. Numerical results demonstrate the superiority of the proposed algorithm and scheme.

In this paper, we study employing movable components on both base station (BS) and intelligent reflecting surface (IRS) in a wideband terahertz (THz) multiple-input-single-output (MISO) system, where the BS is equipped with a movable antenna (MA) array and the IRS consists of movable subarrays. To alleviate double beam squint effect caused by the coupling of beam squint at the BS and IRS, we propose to maximize the minimal received power across a wide THz spectrum by delicately configuring the positions of MAs and IRS subarrays, which is highly challenging. By adopting majorization-minimization (MM) methodology, we develop an algorithm to tackle the aforementioned optimization. Numerical results demonstrate the effectiveness of our proposed algorithm and the benefit of utilizing movable components on the BS and IRS to mitigate double beam squint effect in wideband THz communications.

Spatial scattering modulation (SSM), an emerging millimeter-wave multiple-input multiple-output (MIMO) modu-lation technique, exploits spatial beam resources to enhance the modulation degree of freedom. However, to address the problem that the detection performance of existing scalar-based maximum likelihood (SML) detector is not optimal and the complexity is too high, this paper develops vector-based maximum likelihood (VML) and low-complexity (LC) detectors for the structural char-acteristics of SSM system receivers, respectively. The complexity of the proposed VML algorithm is slightly higher than that of the traditional SML detection algorithm, while the complexity of the proposed LC detection algorithm is reduced by 50% compared with the traditional SML detection algorithm. Numerical results show that at average bit error probability (ABEP) = 10–5, the signal-to-noise ratio (SNR) required for the VML-based ABEP values is 5.5 dB less than that obtained from SML detection. Moreover, the proposed LC algorithm detection performance also saves SNR of 1 dB over SML detector.

Movable antennas (MAs) have shown significant potential in improving the performance of integrated sensing and communication (ISAC) systems. However, their application in integrated and cost-effective full-duplex (FD) monostatic systems remains underexplored. To bridge this research gap, we develop an MA-ISAC model within an FD monostatic framework, where the self-interference channel is modeled as a function of the antenna position vectors under the near-field channel condition. This model enables antenna position optimization for maximizing the weighted sum of communication capacity and sensing mutual information. The resulting optimization problem is non-convex making it challenging to solve optimally. To address this, we employ the fractional programming (FP) method and propose an alternating optimization (AO) algorithm that jointly optimizes the beamforming and antenna positions at the transceivers. Specifically, closed-form solutions for the transmit and receive beamforming matrices are derived using the Karush-Kuhn-Tucker (KKT) conditions, and a novel coarse-to-fine grained searching (CFGS) approach is used to determine high-quality sub-optimal antenna positions. Numerical results demonstrate that with strong self-interference cancellation (SIC) capabilities, MAs significantly enhance the overall performance and reliability of the ISAC system when utilizing our proposed algorithm, compared to conventional fixed-position antenna designs.

Intelligent reflecting surface (IRS) and movable antenna (MA)/fluid antenna (FA) techniques have both received increasing attention in the realm of wireless communications due to their ability to reconfigure and improve wireless channel conditions. In this paper, we investigate the integration of MAs/FAs into an IRS-assisted wireless communication system. In particular, we consider the downlink transmission from a multi-MA base station (BS) to a single-antenna user with the aid of an IRS, aiming to maximize the user's received signal-to-noise ratio (SNR), by jointly optimizing the BS/IRS active/passive beamforming and the MAs' positions. Due to the similar capability of MAs and IRS for channel reconfiguration, we first conduct theoretical analyses of the performance gain of MAs over conventional fixed-position antennas (FPAs) under the line-of-sight (LoS) BS-IRS channel and derive the conditions under which the performance gain becomes more or less significant. Next, to solve the received SNR maximization problem, we propose an alternating optimization (AO) algorithm that decomposes it into two subproblems and solve them alternately. Numerical results are provided to validate our analytical results and evaluate the performance gains of MAs over FPAs under different setups.

Abstract With the growing demand for seamless connectivity and intelligent communication, the integration of artificial intelligence (AI) and sixth-generation (6G) communication networks has emerged as a transformative paradigm. By embedding AI capabilities across various network layers, this integration enables optimized resource allocation, improved efficiency, and enhanced system robust performance. This paper presents a comprehensive overview of AI and communication for 6G networks, with a focus on their foundational principles, inherent challenges, and future research opportunities. We first review the integration of AI and communications in the context of 6G, exploring the driving factors behind incorporating AI into wireless communications, as well as the vision for the convergence of AI and 6G. The discourse then transitions to a detailed exposition of the envisioned integration of AI within 6G networks, divided into three progressive stages. The first stage, AI for network, focuses on employing AI to augment network performance, optimize efficiency, and enhance user service experiences. The second stage, network for AI, highlights the role of the network in facilitating and buttressing AI operations and presents key enabling technologies. We compare wireless network large models with conventional large language models (LLMs), and identify key design principles and components for building wireless network architectures. In the final stage, AI as a service, it is anticipated that future 6G networks will innately provide AI functions as services, supporting application scenarios like immersive communication and intelligent industrial robots. Specifically, we define the quality of AI service, which refers to a framework for measuring AI services within the network. We further summarize the standardization process of AI for wireless networks, highlighting key milestones and ongoing efforts. In addition, we analyze the critical challenges faced by the integration of AI and communications in 6G. Finally, we outline promising future research opportunities that are expected to drive the development and refinement of AI and 6G communications.

In this article, a novel multistream downlink communication system based on the transmissive reconfigurable intelligent surface (RIS) transceiver is proposed. Specifically, a transmissive RIS transceiver architecture is first elaborated, where the downlink communication mechanism and the difference from the conventional multiantenna transceivers are introduced, respectively. More importantly, the generation of RIS element control signals based on time-modulated array (TMA) is illustrated in detail, which can jointly take into account multistream modulation signals and beamforming design. Correspondingly, the harmonic signal extraction scheme at the user is also given. Then, since the design of beamforming has an impact on the system performance, we propose a beamforming optimization algorithm based on matrix lifting, successive convex approximation (SCA) and difference-convex (DC) programming under the constraints of user signal-to-interference-plus-noise ratio (SINR) and available useful power of RIS elements. Furthermore, we analyze the bit error rate (BER) performance of the proposed architecture from two perspectives of transmit multiplexing and transmit diversity. Finally, the convergence behavior of the beamforming algorithm, the impact of different system parameter configurations on system performance and the BER performance of different schemes under the system are verified by numerical simulations.

Recent advancements in integrated sensing and communication (ISAC) technology offer significant potential for high-resolution localization and high-throughput communication in vehicular networks. However, many existing ISAC models treat vehicles as point-like objects, which oversimplifies real-world scenarios. In practice, vehicles have complex shapes and sizes, which may occupy multiple range and angle grids. Additionally, the limited transmit power of roadside units (RSUs) and the small radar cross section (RCS) of vehicles can result in weak echo signals, hindering effective vehicle detection and tracking. To address these challenges, we propose an intelligent omni-surface (IOS) mounted on the top surface of an extended vehicle and introduce a novel IOS-aided extended vehicle tracking scheme. Our approach optimizes both RSU beamforming and IOS configuration (including refraction and reflection amplitudes and phase shifts) to minimize the Cramér-Rao bound (CRB) while meeting communication rate requirements. Solving this optimization problem is challenging due to the complex variable-coupling and the implicit CRB expression. To overcome these difficulties, we present a zeroth-order optimization based increasing penalty dual decomposition (ZO-IPDD) algorithm. Additionally, a dimension reduction strategy is employed to mitigate the high computational complexity. Numerical results demonstrate the effectiveness of the proposed ZO-IPDD algorithm and the superior performance of the tracking scheme compared to existing methods.

Non-Terrestrial Networks (NTNs), as one core infrastructure of the sixth-generation (6G) communication technology, integrate heterogeneous nodes, such as High Earth Orbit (HEO) satellites, to achieve three-dimensional ubiquitous connectivity. However, NTNs face with spectrum scarcity, imposing stringent demands on spectral efficiency and interference management. To address these challenges, we propose a NOMA-oriented spectrum sensing technique for uplink scenarios, where High-Altitude Platforms (HAPs) serve as dynamic aerial nodes for opportunistic transmission within HEO coverage. Specifically, we derive multi-user sensing thresholds to optimize detection accuracy and suppress false alarms. Numerical simulations demonstrate the technique achieves a 33.5% throughput gain over benchmarks at 10 dB and maintains satisfactory performance across PUs’ varying elevation angles and transmission willingness.

In this work, we investigate an active reconfigurable intelligent surface (RIS)-aided non-orthogonal multiple access (NOMA)-enabled space-air-ground integrated network (SAGIN) with cognitive radio, leveraging the flexible deployment of an unmanned aerial vehicle (UAV) and the ubiquitous coverage of satellite networks. The UAV serves uplink and downlink users in the secondary network via NOMA and time division multiple access mechanisms, respectively, while satellites provide wireless backhaul for the UAV and primary users. We aim to maximize the weighted sum mean rate and energy efficiency for the secondary network by jointly the optimizing power allocation, the RIS reflection coefficients (RC), the user matching factors, and the UAV trajectory. We propose an alternating optimization framework based on the block coordinate ascent (BCA) technique, which decouples the problem into multiple variable blocks for alternating optimization until convergence. Moreover, we investigate the performance of energy-efficient active RIS with a sub-connected architecture, decoupling the RIS RC optimization into amplification factor and phase shift subproblems to be solved separately. Finally, simulation results validate the effectiveness of the proposed schemes, and demonstrate weakness of passive RIS and rationality and economics of sub-connected active RIS architecture.

Satellite communication (SatCom) is regarded as a key enabler for bridging connectivity and capacity gaps in sixth-generation (6G) networks. However, the proliferation of Low Earth Orbit (LEO) satellites raises significant intersystem interference risks with Geostationary Earth Orbit (GEO) systems. This paper introduces a cooperative multi-satellite multi-reconfigurable intelligent surface (RIS) transmission framework to mitigate such interference while enhancing LEO SatCom performance. Specifically, cooperative beamforming is designed under a non-coherent cell-free paradigm, considering both adaptive and max ratio (MR) precoding, as well as statistical and two-timescale channel state information (CSI), aiming to synthesize the advantages of cell-free and RIS into SatCom in a practical way. Firstly, an alternating optimization (AO)-based design leveraging statistical CSI with adaptive precoding is proposed. Then, we propose a power allocation algorithm under MR precoding with given RIS phase shifts obtained from the former, along with a direct two-stage design bypassing prior results. Additionally, we extend derived closed-form expressions and proposed algorithms to exploit two-timescale CSI. Numerical results demonstrate the impact of intersystem interference mitigation constraints, compare the performance of proposed algorithms, draw insights into the effects of transmit power, interference threshold, and Rician factors, validate SatCom performance enhancements achieved by RISs, and discuss the advantages of multi-satellite cooperation.

In this paper, we propose a novel transmissive reconfigurable intelligent surface (TRIS) transmitter-enabled spatial modulation (SM) multiple-input multiple-output (MIMO) system. In the transmission stage, a column-control activation strategy is implemented for the TRIS panel, where the specific column elements are activated per time slot. Concurrently, the receiver employs the maximum likelihood detection technique. Based on this, for the transmit signals, we derive the closed-form expressions for the upper bounds of the average bit error probability (ABEP) of the proposed scheme from different perspectives, employing both vector-based and element-based approaches. Furthermore, we provide the asymptotic closed-form expressions for the ABEP of the TRIS-SM scheme, as well as the diversity gain. To improve the performance of the proposed TRIS-SM system, we optimize ABEP with a fixed data rate. Additionally, we provide lower bounds to simplify the computational complexity of improved TRIS-SM scheme. The Monte Carlo simulation method is used to validate the theoretical derivations exhaustively. The results demonstrate that the proposed TRIS-SM scheme can achieve better ABEP performance compared to the conventional SM scheme. Furthermore, the improved TRIS-SM scheme outperforms the TRIS-SM scheme in terms of reliability.

This study addresses the challenges of near-field interference suppression and resource allocation in extremely large-scale multiple-input multiple-output (XL-MIMO) communication systems, particularly under dense-user scenarios. We propose a quality-of-service (QoS)-aware joint user scheduling and power control scheme. Leveraging the spherical wave (SW) characteristics of near field channels, a dual-domain interference suppression strategy is developed by analyzing the spatial correlation of beam focusing vectors in terms of both angular separation and distance constraints. Based on this, a spatial correlation-based scheduling (SCS) algorithm is designed. By integrating this user selection strategy with a dynamic power allocation mechanism, the proposed approach optimizes the sum spectral efficiency while ensuring the user QoS. This framework is further extended to modular XL-MIMO systems. We show how modular deployment can enhance spatial resolution and develop an adapted QoS-aware user scheduling algorithm, called modular SCS (SCS-mod), for this architecture. Simulation results validate that the proposed algorithms significantly outperform existing schemes in terms of sum spectral efficiency and the number of scheduled users, especially under high user density and high transmission power conditions.

To meet the growing demand for data traffic, spectrum-rich optical wireless communication (OWC) has emerged as a key technological driver for the development of 6G. The resonant beam communication (RBC) system, which employs spatially separated laser cavities as the transmitter and receiver, is a high-speed OWC technology capable of self-alignment without tracking. However, its transmission through the air is susceptible to losses caused by obstructions. In this paper, we propose an intelligent reflecting surface (IRS) assisted RBC system with the optical frequency doubling method, where the resonant beam in frequency-fundamental and frequency-doubled is transmitted through both direct line-of-sight (LoS) and IRS-assisted channels to maintain steady-state oscillation and enable communication without echo-interference, respectively. Then, we establish the channel model based on Fresnel diffraction theory under the near-field optical propagation to analyze the transmission loss and frequency-doubled power analytically. Furthermore, communication power can be maximized in real-time by dynamically controlling the beam-splitting ratio between the two channels according to the varying loss levels encountered over air. Numerical results validate that the IRS-assisted channel can compensate for the losses in the obstructed LoS channel and misaligned receivers, ensuring that communication performance reaches an optimal value with dynamic ratio adjustments.

Channel acquisition presents a major challenge in deploying intelligent reflecting surfaces (IRS) aided communication systems, due to massive reflective elements that create a complex multi-path channel and increase channel dimensions. For an IRS-aided communication system, complete channel includes three parts: From the users to IRS, from the IRS to BS, and from the BS back to the IRS. Thus, a generalized multi-IRS cascaded communication system with three cascaded IRSs is considered. Unfortunately, existing channel estimation methods focus on single or double IRS cascades, which is not applicable to the case of triple cascaded IRS channel estimation directly. In this paper, we study the uplink channel estimation for triple cascaded IRSs aided single-user single-input single-output (SISO) systems. Specifically, the triple IRS cascaded channel is typically sparse. It permits us to characterize the channel estimation as a problem of sparse matrix recovery. Then, the sparse learning is explored to achieve robust channel estimation with limited training overhead. Particularly, the sparse channel matrices of the cascaded triple IRS channels have a common row-column block sparsity structure. However, a unique challenge lies in characterizing and enhancing such a common row-column sparsity. To tackle this issue, we apply a random matrix prior to promote the common row-column-wise sparsity of the channel matrix, and then an efficient Bayesian tensor inference algorithm is proposed to estimate the IRS channel. Finally, simulation results confirm that the proposed scheme outperforms traditional counterparts in terms of accuracy.

This paper presents a novel multi-stream downlink communication system that utilizes a transmissive reconfigurable intelligent surface (RIS) transceiver. Specifically, we elaborate the downlink communication scheme using time-modulated array (TMA) technology, which enables high order modulation and multi-stream beamforming. Then, an optimization problem is formulated to maximize the minimum signal-to-interference-plus-noise ratio (SINR) with user fairness, which takes into account the constraint of the maximum available power for each transmissive element. Due to the non-convex nature of the formulated problem, finding optimal solution is challenging. To mitigate the complexity, we propose a linear-complexity beamforming algorithm based on consensus alternating direction method of multipliers (ADMM). Specifically, by introducing a set of auxiliary variables, the problem can be decomposed into multiple sub-problems that are amenable to parallel computation, where the each sub-problem can yield closed-form expressions, bringing a significant reduction in the computational complexity. The overall problem achieves convergence by iteratively addressing these sub-problems in an alternating manner. Finally, the convergence of the proposed algorithm and the impact of various parameter configurations on the system performance are validated through numerical simulations.

In this paper, we investigate a secure communication architecture based on unmanned aerial vehicle (UAV), which enhances the security performance of the communication system through UAV trajectory optimization. We formulate a control problem of minimizing the UAV flight path and power consumption while maximizing secure communication rate over infinite horizon by jointly optimizing UAV trajectory, transmit beamforming vector, and artificial noise (AN) vector. Given the non-uniqueness of optimization objective and significant coupling of the optimization variables, the problem is a non-convex optimization problem which is difficult to solve directly. To address this complex issue, an alternating-iteration technique is employed to decouple the optimization variables. Specifically, the problem is divided into three subproblems, i.e., UAV trajectory, transmit beamforming vector, and AN vector, which are solved alternately. Additionally, considering the susceptibility of UAV trajectory to disturbances, the model predictive control (MPC) approach is applied to obtain UAV trajectory and enhance the system robustness. Numerical results demonstrate the superiority of the proposed optimization algorithm in maintaining accurate UAV trajectory and high secure communication rate compared with other benchmark schemes.

In this paper, we propose a transmissive reconfigurable intelligent surface (TRIS)-empowered distributed cooperative integrated sensing and communication (ISAC) network, which enhances the coverage and wireless environment understanding through the joint design of cooperative users (CUEs) and destination users (DUEs). Rate-splitting multiple access (RSMA) is implemented at the base station (BS), where the common stream is decoded and recoded by the CUEs and forwarded to the DUEs, while the private stream meets the CUEs’ own communication requirements. We construct an optimization problem with the objective of maximizing the minimum Radar mutual information (RMI), and jointly optimize the BS beamforming matrix, the CUE beamforming matrixs, common stream rate, and user scheduling vectors. To address the challenges of the nonconvex optimization problem, the consensus alternating direction multiplier framework (ADMM) is utilized to decouple the variables, and the subproblems are solved independently through iterative optimization until overall convergence is achieved. Numerical results validate the superiority of the proposed scheme in terms of improving communication sum-rate and RMI, and greatly reduce the algorithm complexity.

In this paper, we propose a novel transmissive reconfigurable intelligent surface (TRIS) transceiver-enhanced robust and secure integrated sensing and communication (ISAC) network. A time-division sensing communication mechanism is designed for the scenario, which enables communication and sensing to share wireless resources. To address the interference management problem and hinder eavesdropping, we implement rate-splitting multiple access (RSMA), where the common stream is designed as a useful signal and an artificial noise (AN), while taking into account the imperfect channel state information and modeling the channel for the illegal users in a fine-grained manner as well as giving an upper bound on the error. We introduce the secrecy outage probability and construct an optimization problem with secrecy sum-rate as the objective functions to optimize the common stream beamforming matrix, the private stream beamforming matrix and the timeslot duration variable. Due to the coupling of the optimization variables and the infinity of the error set, the proposed problem is a nonconvex optimization problem that cannot be solved directly. In order to address the above challenges, the block coordinate descent (BCD)-based second-order cone programming (SOCP) algorithm is used to decouple the optimization variables and solving the problem. Specifically, the problem is decoupled into two subproblems concerning the common stream beamforming matrix, the private stream beamforming matrix, and the timeslot duration variable, which are solved by alternating optimization until convergence is reached. To solve the problem, S-procedure, Bernstein’s inequality and successive convex approximation (SCA) are employed to deal with the objective function and non-convex constraints. Numerical simulation results verify the superiority of the proposed scheme in improving the secrecy energy efficiency (SEE) and the Cramér-Rao boundary (CRB).

Different types of intelligent reflecting surfaces (IRS) are exploited for assisting wireless communications. The joint use of passive IRS (PIRS) and active IRS (AIRS) emerges as a promising solution owing to their complementary advantages. They can be integrated into a single hybrid active-passive IRS (HIRS) or deployed in a distributed manner, which poses challenges in determining the IRS element allocation and placement for rate maximization. In this paper, we investigate the capacity of an IRS-aided wireless communication system with both active and passive elements. Specifically, we consider three deployment schemes: 1) base station (BS)$\rightarrow $HIRS$\rightarrow $user (BHU); 2) BS$\rightarrow $AIRS$\rightarrow $PIRS$\rightarrow $user (BAPU); 3) BS$\rightarrow $PIRS$\rightarrow $AIRS$\rightarrow $user (BPAU). Under the line-of-sight channel model, we formulate a rate maximization problem via a joint optimization of the IRS element allocation and placement. We first derive the optimized number of active and passive elements for BHU, BAPU, and BPAU schemes, respectively. Then, low-complexity HIRS/AIRS placement strategies are provided. To obtain more insights, we characterize the system capacity scaling orders for the three schemes with respect to the large total number of IRS elements, amplification power budget, and BS transmit power. Finally, simulation results are presented to validate our theoretical findings and show the performance difference among the BHU, BAPU, and BPAU schemes with the proposed joint design under various system setups.

Maximizing energy efficiency in VR 360 video transmission is essential for advancing VR applications. Motivated by this goal, we conduct a comprehensive study that integrates the characteristics of VR 360 video with beamforming strategies and intelligent reflecting surface (IRS) shifting techniques in the rate-splitting multiple access (RSMA) network. In the IRS-aided RSMA network, we propose a stable energy-efficient transmission (SEET) scheme aimed at minimizing the number of transmitted VR video chunks. The SEET scheme constructs a stable pre-transmission and playback flow, ensuring seamless and continuous display of the upcoming content without latency. We also propose a mixed-format-based chunk (MFC) method that simultaneously pre-transmits both 2D and 3D chunk frames to each user, further enhancing energy efficiency. We utilize an alternating optimization method to divide the original energy-efficient problem into three subproblems. To tackle the non-convex and NP-hard beamforming subproblem, we utilize the first-order Taylor expansion and then obtain the approximate transmission rates of common messages and private messages regarding the quadratic form of beamforming vectors. We then utilize quadratically constrained programming, fractional programming, and linear programming to obtain the near-optimal solutions for beamforming vectors, IRS phase shifts, and RSMA parameters, respectively. The final numerical results affirm that the proposed SEET scheme can notably minimize the beamforming power of the base station. Through the SEET scheme, the MFC method with the approximation method exhibits superior energy efficiency, outperforming existing transmission methods in terms of both energy utility and consumption by HMDs.

In this paper, we propose an efficient joint precoding design method to maximize the weighted sum-rate in wideband intelligent reflecting surface (IRS)-assisted cell-free networks by jointly optimizing the active beamforming of base stations and the passive beamforming of IRS. Due to employing wideband transmissions, the frequency selectivity of IRSs has to been taken into account, whose response usually follows a Lorentzian-like profile. To address the high-dimensional non-convex optimization problem, we employ a fractional programming approach to decouple the non-convex problem into subproblems for alternating optimization between active and passive beamforming. The active beamforming subproblem is addressed using the consensus alternating direction method of multipliers (CADMM) algorithm, while the passive beamforming subproblem is tackled using the accelerated projection gradient (APG) method and Flecher-Reeves conjugate gradient method (FRCG). Simulation results demonstrate that our proposed approach achieves significant improvements in weighted sum-rate under various performance metrics compared to primal-dual subgradient (PDS) with ideal reflection matrix. This study provides valuable insights for computational complexity reduction and network capacity enhancement.

Unmanned aerial vehicles (UAVs) have received plenty of attention due to their high flexibility and enhanced communication ability, nonetheless, the limited onboard energy restricts UAVs’ application on persistent data collection missions in large areas. In this paper, we propose a rechargeable UAV-assisted periodic data collection scheme, where a UAV is dispatched to periodically collect data from sensor nodes (SNs) in the mission area and charged by a wireless charging platform. Specifically, the periodic data collection completion time is minimized by optimizing the UAV trajectory to reach the optimal balance among the collection time, flight time, and recharging time. The formulated problem is non-convex and difficult to solve directly. To tackle this problem, we divide the main problem into two sub-problems and address them by leveraging successive convex approximation (SCA), bisection search, and heuristic methods. Then, we propose a periodic trajectory optimization algorithm to iteratively solve the two sub-problems to minimize the completion time. Furthermore, to deal with the dynamics of SNs, we propose a low-complexity trajectory adjustment strategy, where the trajectory can be maintained or adjusted locally at the SNs change, which significantly mitigates the computation cost of re-optimization. The simulation results show the superiority and robustness of the proposed scheme and the completion time is on average 39% and 33% lower than the two benchmarks, respectively.

Autonomous aerial vehicle (AAV) can be threatening to the information security of wireless communications. By launching the pilot spoofing attack (PSA), a AAV, operating as the active aerial-eavesdropper (A-Eve), is able to intercept the confidential messages sent over the air. On one hand, it is difficult to distinguish the channel state information (CSI) of the ground users (GUs) and the CSI of A-Eve in the contaminated pilots. On the other hand, due to the high-mobility of A-Eve, the CSI of A-Eve is rapidly changing, making the design of secure transmissions challenging. To address these issues, we first propose a location-based minimum mean square error (MMSE) channel estimation algorithm to separate the CSI of GUs and the CSI of A-Eve, where the location of A-Eve is obtained by designing a cooperative localization neural network (CLNet), leveraging its angular-domain channel fingerprint (CF) of A-Eve. Furthermore, we propose an artificial noise (AN) injected MMSE precoding scheme to maximize the worst-case secrecy rate of the multi-user communications, where the power allocation between signal and AN is optimized via a long short-term memory (LSTM)-based secure predictive beamforming neural network (SPBNet). Numerical results verify the secrecy performance gain of the proposed scheme achieved by utilizing the localization ability via the CLNet and the channel tracking ability via the SPBNet, compared to the canonical nullspace AN injection scheme without prior knowledge of A-Eve’s location.

Movable antenna (MA) and intelligent reflecting surface (IRS) are considered promising technologies for the next-generation wireless communication systems due to their shared capabilities of reconfiguring and improving wireless channel conditions. This, however, raises a fundamental question: Does the performance gain of MAs over conventional fixed-position antennas (FPAs) still exist in the presence of the IRS passive beamforming? To answer this question, we investigate in this paper an IRS-assisted multi-user multiple-input single-output (MISO) MA system, where a multi-MA base station (BS) transmits to multiple single-FPA users. We formulate a sum-rate maximization problem by jointly optimizing the active/passive beamforming of the BS/IRS and the MA positions within a one-dimensional transmit region, which is challenging to be optimally solved. To drive essential insights, we first study a simplified case with a single user. Then, we analyze the performance gain of MAs over FPAs in the light-of-sight (LoS) BS-IRS channel and derive the conditions under which this gain becomes more or less significant. In addition, we propose an alternating optimization (AO) algorithm to solve the signal-to-noise ratio (SNR) maximization problem in the single-user case by combining the block coordinate descent (BCD) method and the graph-based method. For the general multi-user case, our performance analysis unveils that the performance gain of MAs over FPAs diminishes with typical transmit precoding strategies at the BS under certain conditions. We also propose a high-quality suboptimal solution to the sum-rate maximization problem by applying the AO algorithm that combines the weighted minimum mean square error (WMMSE) algorithm, manifold optimization method and discrete sampling method. Numerical results validate our theoretical analyses and demonstrate that the performance gain of MAs over FPAs may be reduced if the IRS passive beamforming is optimized.

To overcome the impact of information leakage, obstacle blocking, channel uncertainties, and hardware impairments (HWIs) in wireless communication systems, we design a robust secure transmission strategy for a multi-reconfigurable intelligent surface (RIS)-aided communication system with HWIs and channel uncertainties, where a multi-antenna base station (BS) serves multiple wireless users aided by multiple RISs and overcomes information leakage caused by multiple eavesdroppers. Based on bounded channel uncertainties, a total transmit power minimization problem is investigated subject to the secrecy rates of users, the maximum transmit power of the BS, and the phase shifts of RISs. To deal with the formulated non-convex problem with parameter perturbations, it is transformed into a deterministic problem by using the worst-case approach, S-procedure, and successive convex approximation. Then, the problem is decomposed into an active beamforming and artificial noise subproblem and a passive beamforming subproblem. The subproblems are converted into convex ones via the semi-definite relaxation method, singular value decomposition, penalty function, and eigenvalue decomposition approaches. Finally, an iteration-based robust resource allocation algorithm is proposed. Simulation results verify that by deploying more RISs or increasing the number of reflection elements, the impacts of eavesdroppers and HWIs can be effectively decreased even with channel estimation errors.

In order to reduce the impact of obstacles and improve system performance for traditional backscatter communication (BackCom) networks, we propose a reconfigurable intelligent surface (RIS)-assisted heterogeneous BackCom network framework, where multiple backscatter clusters share the spectrum resource with macrocell users in an underlay spectrum sharing mode and achieve self-sufficient energy of each low-power-consumption backscatter device (BD) via a radio-frequency energy-harvesting way. Then, a robust resource allocation problem with imperfect channel station information is studied under the constraints of the minimum rate requirement of each BD, the minimum energy requirement of each BD, the maximum interference power of each macrocell user, the reflection coefficient of each BD, and the phase shifts of each RIS. Moreover, based on the bounded channel uncertainty model, a max-min throughput resource allocation problem of multiple backscatter clusters is formulated by jointly optimizing the time allocation factors, the reflection coefficient of each BD, and the phase shifts of each RIS. To deal with the non-convex optimization problem caused by the uncertain constraints and non-convex constraints, the worst-case approach, successive convex approximation as well as semi-definite relaxation are applied. Finally, an iteration-based robust resource allocation algorithm is proposed accordingly. Simulation results demonstrate that the proposed algorithm has good fairness and stronger robustness.

In this paper, we consider the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted THz communications with three-side beam split. Except for the beam split at the base station (BS), we analyze the double-side beam split at the STAR-RIS for the first time. To relieve the double-side beam split effect, we first propose a time delayer (TD)-based fully-connected structure at the STAR-RIS. As a further advance, a low-hardware complexity and low-power consumption sub-connected structure is developed, where multiple STAR-RIS elements share one TD. Meanwhile, considering the practical scenario, we investigate a multi-STAR-RIS and multi-user communication system, and sum rate maximization problem is formulated by jointly optimizing the hybrid analog/digital beamforming, time delays at the BS as well as the double-layer phase shift coefficients, time delays and amplitude coefficients at the STAR-RISs. Based on this, we first allocate users for each STAR-RIS, and then derive the analog beamforming, time delays at the BS, and the double-layer phase shift coefficients, time delays at each STAR-RIS. Next, we develop an alternative optimization algorithm to calculate the digital beamforming at the BS and amplitude coefficients at the STAR-RISs. Finally, the numerical results verify the effectiveness of the proposed schemes.

In this paper, we investigate the near-field wideband terahertz (THz) massive multi-input multi-output (MIMO) integrated sensing and communication (ISAC) systems. Specifically, we propose an energy-efficient serial true-time-delay (TTD)-based reconfigurable antenna architecture for the dual-function base station (DFBS). This architecture enables DFBS to switch between modular and compact MIMO configurations by dynamically controlling the activation of antenna subarrays, corresponding to the sensing and communication stages, respectively. During the sensing stage, a modular MIMO architecture is deployed by deactivating several subarrays and all TTDs. This configuration utilizes fewer antennas to achieve a larger array aperture with enhanced energy efficiency. Moreover, we take advantage of beam squint effects at both the main and grating lobes to extend sensing range and enable rapid user sensing. During the communication stage, a compact MIMO configuration is employed by activating all antennas, and the TTD network is activated to mitigate the near-field beam squint effect. Based on the channel state information obtained during the sensing stage, we formulate a joint optimization problem of the hybrid analog/digital beamforming and TTD network time delays to maximize the system sum rate. To solve it, we first design analog beamforming and time delays through a serial-delay approach, followed by an alternating iterative optimization for digital beamforming. In addition, our proposed scheme accounts for the finite resolution and limited delay range of TTDs. Simulation results demonstrate the effectiveness of our designed antenna architecture and optimization scheme.

In this paper, the sensing beam pattern gain under simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS)-enabled integrated sensing and communications (ISAC) systems is investigated, in which the dual-functional base station (DFBS) provides multiple targets sensing in the presence of environment clutters and communicates with multiple users simultaneously. However, multiple targets detection introduces new challenges, since the STAR-RIS cannot directly send sensing beams and detect targets, the DFBS is required to analyze the echoes of the targets. While the echoes reflected by different targets through STAR-RIS come from the same direction for the DFBS, making it difficult to distinguish them. To circumvent this issue, we first introduce the signature sequence (SS) modulation scheme to the STAR-RIS-enabled ISAC system, thus ensuring that DFBS can detect different targets through the SS modulated sensing beams. Next, via the joint beamforming design of DFBS and STAR-RIS, we develop a max-min sensing beam pattern gain problem, and meanwhile, considering the communication quality requirements, the interference limitations of multi-targets and clutters, the passive nature constraint of STAR-RIS, and the total transmit power limitation. Then, to tackle the complex non-convex problem, we propose an alternating optimization method to divide it into two sub-problems and iteratively solve them until convergence. For the former, by relaxing the rank-one constraint, the problem is transformed into the standard convex quadratic semi-definite program and can be solved through the semi-definite relaxation and semi-definite programming algorithms. For the latter, the penalty-based algorithm is used to convert the rank-one constraints as penalty terms to the objective function, and the successive convex approximation method is leveraged to solve it. Finally, simulation results are conducted to validate the benefits and efficiency of our proposed scheme.

Integrated sensing and communication (ISAC) systems may face a heavy computation burden since the sensory data needs to be further processed. This paper studies a novel system that integrates sensing, communication, and computation, aiming to provide services for different objectives efficiently. This system consists of a multi-antenna multi-functional base station (BS), an edge server, a target, and multiple single-antenna communication users. The BS needs to allocate the available resources to efficiently provide sensing, communication, and computation services. Due to the heavy service burden and limited power budget, the BS can partially offload the tasks to the nearby edge server instead of computing them locally. We consider the estimation of the target response matrix, a general problem in radar sensing, and utilize Cramér-Rao bound (CRB) as the corresponding performance metric. To tackle the non-convex optimization problem, we propose both semidefinite relaxation (SDR)-based alternating optimization and SDR-based successive convex approximation (SCA) algorithms to minimize the CRB of radar sensing while meeting the requirement of communication users and the need for task computing. Furthermore, we demonstrate that the optimal rank-one solutions of both the alternating and SCA algorithms can be directly obtained via the solver or further constructed even when dealing with multiple functionalities. Simulation results show that the proposed algorithms can provide higher target estimation performance than state-of-the-art benchmarks while satisfying the communication and computation constraints.

Movable antennas (MAs), which can be swiftly repositioned within a defined region, offer a promising solution to the limitations of fixed-position antennas (FPAs) in adapting to spatial variations in wireless channels, thereby improving channel conditions and communication between transceivers. However, frequent MA position adjustments based on instantaneous channel state information (CSI) incur high operational complexity, making real-time CSI acquisition impractical, especially in fast-fading channels. To address these challenges, we propose a two-timescale transmission framework for MA-enabled multiuser multiple-input-multiple-output (MU-MIMO) systems. In the large timescale, statistical CSI is exploited to optimize MA positions for long-term ergodic performance, whereas, in the small timescale, beamforming vectors are designed using instantaneous CSI to handle short-term channel fluctuations. Within this new framework, we analyze the ergodic sum rate and develop efficient MA position optimization algorithms for both maximum-ratio-transmission (MRT) and zero-forcing (ZF) beamforming schemes. These algorithms employ alternating optimization (AO), successive convex approximation (SCA), and majorization-minimization (MM) techniques, iteratively optimizing antenna positions and refining surrogate functions that approximate the ergodic sum rate. Numerical results show significant ergodic sum rate gains with the proposed two-timescale MA design over conventional FPA systems, particularly under moderate to strong line-of-sight (LoS) conditions. Notably, MA with ZF beamforming consistently outperforms MA with MRT, highlighting the synergy between beamforming and MAs for superior interference management in environments with moderate Rician factors and high user density, while MA with MRT can offer a simplified alternative to complex beamforming designs in strong LoS conditions.

Extremely Large-scale Array (ELAA) promises to deliver ultra-high data rates with increased antenna elements. However, increasing antenna elements leads to a wider realm of near-field, which challenges the traditional design of codebooks. In this paper, we propose novel near-field codebook schemes based on the fitting formula of codewords’ quantization performance. First, we analyze the quantization performance properties of uniform linear array (ULA) and uniform planar array (UPA) codewords. Our findings reveal an intriguing property: the correlation formula for ULA codewords can be represented by the elliptic formula, while the correlation formula for UPA codewords can be approximated using the ellipsoid formula. Building on this insight, we propose a ULA uniform codebook that maximizes the minimum correlation based on the derived formula. Moreover, we introduce a ULA dislocation codebook to further reduce quantization overhead. Continuing our exploration, we propose UPA uniform and dislocation codebook schemes. Our investigation demonstrates that oversampling in the angular domain offers distinct advantages, achieving heightened accuracy while minimizing overhead in quantifying near-field channels. Numerical results demonstrate the appealing advantages of the proposed codebook over existing methods in decreasing quantization overhead and increasing quantization accuracy.

This paper proposes a millimeter-wave (mmWave) multiple-input multiple-output (MIMO) transmission scheme termed spatial scattering shift keying (SSSK), which exploits spatial scattering modulation (SSM) to encode information through the indices of channel scatterers rather than conventional symbol constellations. The proposed SSSK achieves superior reliability compared to amplitude-phase modulation (APM) schemes, while simultaneously reducing hardware complexity. Specifically, the scatterer-index-based signaling mechanism mitigates the detection complexity inherent in APM systems by avoiding explicit symbol-level demodulation. In addition, we illustrate the advantages of SSSK by investigating the interaction between SSSK and fading channels. We derive closed-form expressions for the average bit error probability (ABEP) tight upper bound of the proposed scheme using two different approaches based on the greedy detection algorithm. To gain more insights, we further derive the asymptotic ABEP expression and diversity gain. To characterize the performance, we rigorously derive tight upper bounds on the ABEP using two complementary approaches: union bound and pairwise error probability analysis under a greedy detection framework. Furthermore, asymptotic ABEP expressions are established to reveal the achievable diversity gain. Moreover, we design maximum likelihood (ML) detectors with serial and parallel architectures and corresponding ABEP upper bounds. Simulations validate the analytical derivations and demonstrate SSSK outperforms APM in ABEP at high signal-to-noise ratios. The proposed greedy detector reduces computational complexity compared to the serial ML detector while maintaining comparable ABEP performance.

Cooperative reconfigurable intelligent surfaces (RISs) are promising technologies for 6 G networks to support a great number of users. Compared with the fixed RISs, the properly deployed RISs may improve the communication performance with less communication energy consumption, thereby improving the energy efficiency. In this paper, we consider a cooperative unmanned aerial vehicle-mounted RISs (UAV-RISs)-assisted cellular network, where multiple RISs are carried and enhanced by UAVs to serve multiple ground users (GUs) simultaneously such that achieving the three-dimensional (3D) mobility and opportunistic deployment. Specifically, we formulate an energy-efficient communication problem based on multi-objective optimization framework (EEComm-MOF) to jointly consider the beamforming vector of base station (BS), the location deployment and the discrete phase shifts of UAV-RIS system so as to simultaneously maximize the minimum available rate over all GUs, maximize the total available rate of all GUs, and minimize the total energy consumption of the system, while the transmit power constraint of BS is considered. To comprehensively solve EEComm-MOF which is an NP-hard and non-convex problem with constraints, a non-dominated sorting genetic algorithm-II with a continuous solution processing mechanism, a discrete solution processing mechanism, and a complex solution processing mechanism (INSGA-II-CDC) is proposed. Simulations results demonstrate that the proposed INSGA-II-CDC can solve EEComm-MOF effectively and outperforms other benchmarks under different parameter settings. Moreover, the stability of INSGA-II-CDC and the effectiveness of the improved mechanisms are verified. Finally, the implementability analysis of the algorithm is given.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) is emerging as a promising paradigm to provide aerial-terrestrial computing services in close proximity to mobile devices (MDs). However, meeting the demands of computation-intensive and delay-sensitive tasks for MDs poses several challenges, including the demand-supply contradiction between MDs and MEC servers, the demand-supply discrepancy between MDs and MEC servers, the trajectory control requirements on energy efficiency and timeliness, and the different time-scale dynamics of the network. To address these issues, we first present a hierarchical architecture by incorporating terrestrial-aerial computing capabilities and leveraging UAV flexibility. Furthermore, we formulate a joint computing resource allocation, computation offloading, and trajectory control problem to maximize the system utility. Since the problem is a non-convex and NP-hard mixed integer nonlinear programming (MINLP), we propose a two-timescale joint computing resource allocation, computation offloading, and trajectory control (TJCCT) approach for solving the problem. In the short timescale, we propose a price-incentive model for on-demand computing resource allocation and a matching mechanism-based method for computation offloading. In the long timescale, we propose a convex optimization-based method for UAV trajectory control. Besides, we theoretically prove the stability and polynomial complexity of TJCCT. Extensive simulation results demonstrate that the proposed TJCCT is able to achieve superior performances in terms of the system utility, average processing rate, average completion delay, average completion ratio, and average cost, while meeting the energy constraints despite the trade-off of the increased energy consumption.

Due to flexibility and low-cost, unmanned aerial vehicles (UAVs) are increasingly crucial for enhancing coverage and functionality of wireless networks. However, incorporating UAVs into next-generation wireless communication systems poses significant challenges, particularly in sustaining high-rate and long-range secure communications against eavesdropping attacks. In this work, we consider a UAV swarm-enabled secure surveillance network system, where a UAV swarm forms a virtual antenna array to transmit sensitive surveillance data to a remote base station (RBS) via collaborative beamforming (CB) so as to resist mobile eavesdroppers. Specifically, we formulate an aerial secure communication and energy efficiency multi-objective optimization problem (ASCEE-MOP) to maximize the secrecy rate of the system and to minimize the flight energy consumption of the UAV swarm. To address the non-convex, NP-hard and dynamic ASCEE-MOP, we propose a generative diffusion model-enabled twin delayed deep deterministic policy gradient (GDMTD3) method. Specifically, GDMTD3 leverages an innovative application of diffusion models to determine optimal excitation current weights and position decisions of UAVs. The diffusion models can better capture the complex dynamics and the trade-off of the ASCEE-MOP, thereby yielding promising solutions. Simulation results highlight the superior performance of the proposed approach compared with traditional deployment strategies and some other deep reinforcement learning (DRL) benchmarks. Moreover, performance analysis under various parameter settings of GDMTD3 and different numbers of UAVs verifies the robustness of the proposed approach.

The low-altitude economy (LAE), as a new economic paradigm, plays an indispensable role in cargo transportation, healthcare, infrastructure inspection, and especially post-disaster communications. Specifically, unmanned aerial vehicles (UAVs), as one of the core technologies of the LAE, can be deployed to provide communication coverage, facilitate data collection, and relay data for trapped users, thereby significantly enhancing the efficiency of post-disaster response efforts. However, conventional UAV self-organizing networks exhibit low reliability in long-range cases due to their limited onboard energy and transmit ability. Therefore, in this paper, we design an efficient and robust UAV-swarm enabled collaborative self-organizing network to facilitate post-disaster communications. Specifically, a ground device transmits data to UAV swarms, which then use collaborative beamforming (CB) technique to form virtual antenna arrays and relay the data to a remote access point (AP) efficiently. Then, we formulate a rescue-oriented post-disaster transmission rate maximization optimization problem (RPTRMOP), aimed at maximizing the transmission rate of the whole network. Given the challenges of solving the formulated RPTRMOP by using traditional algorithms, we propose a two-stage optimization approach to address it.In the first stage, the optimal multi-path traffic routing and the theoretical upper bound on the transmission rate of the network are derived.In the second stage, we transform the formulated RPTRMOP into a variant named V-RPTRMOP based on the obtained optimal multi-path traffic routing, aimed at rendering the actual transmission rate closely approaches its theoretical upper bound by optimizing the excitation current weight and the placement of each participating UAV via a diffusion model-enabled particle swarm optimization (DM-PSO) algorithm. Simulation results show the effectiveness of the proposed two-stage optimization approach in improving the transmission rate of the constructed network, which demonstrates the great potential for post-disaster communications. Moreover, the robustness of the constructed network is also validated via evaluating the impact of three unexpected situations on the system transmission rate.

In disaster scenarios, conventional terrestrial multi-access edge computing (MEC) paradigms, which rely on ground infrastructure, may become unavailable due to infrastructure damage. With high-probability line-of-sight (LoS) communication, flexible mobility, and low cost, uncrewed aerial vehicle (UAV)-assisted MEC is emerging as a promising paradigm to provide edge computing services for ground user devices (UDs) in disaster-stricken areas. However, the limited battery capacity, computing resources, and spectrum resources also pose serious challenges for UAV-assisted MEC, which can potentially shorten the service time of UAVs and degrade the quality of experience (QoE) of UDs without an effective control approach. To this end, in this work, we first present a hierarchical architecture of multiple-UAV-assisted MEC networks that enables the coordinated provision of edge computing services by multiple UAVs. Then, we formulate a joint task offloading, resource allocation, and UAV trajectory control optimization problem (JTRTOP) to maximize the QoE of UDs while considering the energy and resource constraints of UAVs. Since the problem is proven to be a future-dependent and NP-hard problem, we propose a novel online joint task offloading, resource allocation, and UAV trajectory control approach (OJTRTA) to solve the problem. Specifically, the JTRTOP is first transformed into a per-slot real-time optimization problem (PROP) using the Lyapunov optimization framework. Then, a two-stage optimization method based on game theory and convex optimization is proposed to solve the PROP. Simulation results show that the proposed OJTRTA outperforms various benchmark approaches and achieves at least a 10% improvement in the QoE of UDs compared to deep reinforcement learning (DRL)-based algorithms, thereby validating the superiority of the proposed approach.

In this paper, we focus on the joint beamforming for intelligent reflecting surface (IRS) aided integrated sensing and communication (ISAC) systems, where a multi-antenna base station (BS) performs multi-user multi-input single-output (MU-MISO) communication and radar sensing simultaneously. Specifically, the direction-of-arrival (DoA) estimation is considered as the task of radar sensing, and we aim to optimize the MU-MISO communication while enhancing the estimation accuracy by ensuring a Cramér-Rao bound (CRB) lower bound. First, for the CRB-constrained sum rate maximization problem, we propose a product Riemannian manifold optimization (PRMO) framework to solve the problems without relaxing the objective functions. Specifically, a product Riemannian manifold space (PRMS) is constructed to satisfy the constraints of the precoding matrix and IRS phase shifts, and the constraint of the CRB threshold is tackled by a Riemannian exact penalty (REP) method. A parallel Riemannian Broyden-Fletcher–Goldfarb-Shanno (P-RBFGS) algorithm is derived to update the parameters over the PRMS. Then, considering the fairness of the MU-MISO communication, the PRMO is further extended to tackle the CRB-constrained max-min optimization by maximizing the minimum rate among all users. Simulation results demonstrate that with the same CRB constraint, the PRMO outperforms the existing method in sum rate maximization with lower computational complexity, and the extended PRMO enables the users to obtain approximately equal rates, thus guaranteeing the fairness of the system.

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.

This paper investigates an integrated communication and positioning (ICAP) system facilitated by rate-splitting multiple access (RSMA). We propose encoding part of users’ messages into a common stream using a public codebook, which simultaneously facilitates fingerprint-based positioning. To enhance positioning accuracy, we investigate the interplay between geometric and non-geometric spatial consistency, which intriguingly leads to improved quality of imperfect channel state information (ICSI). In particular, we establish a novel strategy to significantly reduce the minimum mean square error of channel estimation via the Bayes pooling principle. We also demonstrate the theoretical equivalence between ICSI enhancement and positioning accuracy. Moreover, we develop a progressive transmission protocol that minimizes training overhead alongside ICSI enhancement strategy while allowing for the reallocation of spare resources without compromising designed performance. Our cooperative ICAP system leverages both reconfigurable intelligent surface and transmit precoding techniques to reconfigure the spatial consistency of the propagation space. Numerical results underscore advantages of proposed system in achieving the broadest Pareto boundary among existing ICAP designs. Furthermore, we reveal that RSMA bolsters communication capabilities while the ICSI enhancement strategy predominantly augments positioning.

Nowadays, unmanned aerial vehicles (UAVs) are deployed to perceive high-definition visuals of ground targets (GTs) for environment reconstruction of virtual reality (VR) by leveraging their high flexibility. Inspired by the classic scalable video coding method, we develop a novel multi-UAV collaborative layered visual perception and transmission scheme for VR named MUL-VR, wherein GTs are divided into multiple overlapped clusters and multiple UAVs are deployed to collaboratively perceive visuals from these clusters. Specifically, our proposed formulation entails maximizing user’s quality of experience (QoE) by optimizing cluster radii, UAV horizontal coordinates, and bandwidth allocation strategy subject to the constraints on visual quality, transmission delay and available bandwidth. To address this issue, we formulate the investigated MUL-VR scheme into an intractable optimization problem, which, however, is difficult to solve due to the non-convexity of the objective function and constraints, as well as the intricate coupling of the variables. To tackle this challenging problem, we first propose an efficient alternating algorithm, which decomposes the original optimization problem into three subproblems, and then derive the optimal closed-form solution to each subproblem. Consequently, the final solution can be obtained by iteratively optimizing the variables associated with each subproblem, while holding the variables in the other two subproblems fixed, until the convergence condition is satisfied. Simulation results demonstrate that the proposed scheme can effectively improve the user’s QoE and enhance the robustness of the system, yielding superior performance compared to other benchmarks. Specifically, compared to the classic K-Means based scheme, the proposed scheme offers a 25.9% enhancement in terms of QoE when the preference coefficient ε = 0.1 and such performance gain progressively expands as ε increases.

In this paper, we propose a novel symbiotic sensing and communication (SSAC) framework, comprising a base station (BS) and a passive sensing node. In particular, the BS transmits communication waveform to serve vehicle users (VUEs), while the sensing node is employed to execute sensing tasks based on the echoes in a bistatic manner, thereby avoiding the issue of self-interference. Besides the weak target of interest, the sensing node tracks VUEs and shares sensing results with BS to facilitate sensing-assisted beamforming. By considering both fully digital arrays and hybrid analog-digital (HAD) arrays, we investigate the beamforming design in the SSAC system. We first derive the Cramér-Rao lower bound (CRLB) of the two-dimensional angles of arrival estimation as the sensing metric. Next, we formulate an achievable sum rate maximization problem under the CRLB constraint, where the channel state information is reconstructed based on the sensing results. Then, we propose two penalty dual decomposition (PDD)-based alternating algorithms for fully digital and HAD arrays, respectively. Simulation results demonstrate that the proposed algorithms can achieve an outstanding data rate with effective localization capability for both VUEs and the weak target. In particular, the HAD beamforming design exhibits remarkable performance gain compared to conventional schemes, especially with fewer radio frequency chains.

Extremely large-scale multiple-input multiple-output (XL-MIMO) with orthogonal frequency division multiplexing (OFDM) transmission can provide unprecedented improvement in spectral efficiency and data rate. Dynamic metasurface antennas (DMAs) have been proposed as a cost-effective and power-efficient solution for realizing XL-MIMO systems. However, the extremely large number of antennas in XL-MIMO-OFDM with DMAs poses critical challenges in acquiring accurate channel state information. To address this issue, we propose in this paper a tensor-based channel estimation method for frequency-selective XL-MIMO-OFDM systems with DMAs. We first characterize the configurable property of DMAs and propose a microstrip-sequential channel training method with quasi-dynamically adjustable metamaterial elements, by representing the received frequency-domain training signals as a fourth-order tensor which admits the canonical polyadic decomposition. Then, by exploiting the sparsity of XL-MIMO channels, we propose a two-stage tensor decomposition-based channel estimation algorithm, where the four coupling factor matrices are obtained without the need of iterative refinement, and the channel multipath parameters can be extracted for reconstructing the entire high-dimensional channel matrix. In addition, we analyze the uniqueness condition for the proposed tensor-based channel estimation method, which reveals that the required channel training overhead is only proportional to the number of channel multipaths, instead of that of metamaterial elements and microstrips. Numerical results demonstrate the superior performance of our proposed design with significantly reduced training overhead as compared to various benchmark schemes.

This paper studies a multiple intelligent reflecting surfaces (IRSs) collaborative localization system where multiple semi-passive IRSs are deployed in the network to locate one or more targets based on time-of-arrival. It is assumed that each semi-passive IRS is equipped with reflective elements and sensors, which are used to establish the line-of-sight links from the base station (BS) to multiple targets and process echo signals, respectively. Based on the above model, we derive the Fisher information matrix of the echo signal with respect to the time delay. By employing the chain rule and exploiting the geometric relationship between time delay and position, the Cramér-Rao bound (CRB) for estimating the target’s Cartesian coordinate position is derived. Then, we propose a two-stage algorithmic framework to minimize CRB in single- and multi-target localization systems by joint optimizing active beamforming at BS, passive beamforming at multiple IRSs and IRS selection. For the single-target case, we derive the optimal closed-form solution for multiple IRSs coefficients design and propose a low-complexity algorithm based on alternating direction method of multipliers to obtain the optimal solution for active beaming design. For the multi-target case, alternating optimization is used to transform the original problem into two subproblems where semi-definite relaxation and successive convex approximation are applied to tackle the quadraticity and indefiniteness in the CRB expression, respectively. Finally, numerical simulation results validate the effectiveness of the proposed algorithm for multiple IRSs collaborative localization system compared to other benchmark schemes as well as the significant performance gains.

Spatial scattering modulation (SSM), an emerging millimeter-wave (mmWave) multiple-input multiple-output (MI-MO) modulation technique, exploits spatial beam resources to enhance the modulation degree of freedom. However, to address the problem that the detection performance of existing scalarbased maximum likelihood (SML) detector is not optimal and the complexity is too high, this paper develops vector-based maximum likelihood (VML) and low-complexity (LC) detectors for the structural characteristics of SSM system receivers, respectively. Then, we give corresponding analysis for the complexity of each of the three detectors. Based on the SML, VML, and LC detectors, we derive the union upper bound of average bit error probability (ABEP) for the SSM scheme, respectively. Monte Carlo simulations validate the correctness of the analytical derivation and show that when ABEP = 10–5, the signal-to-noise ratio (SNR) required for the VML-based ABEP values is 5.5 dB less than that obtained from SML detection. Moreover, compared with SML, the detection complexity of the proposed LC algorithm is reduced by about 50% and the transmit SNR also saves 1 dB SNR. Furthermore, when the number of scatterers is higher, the ABEP performance advantage of the SSM system is more fully unlocked.

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.

Unmanned aerial vehicles (UAVs)-enabled aerial communication provides a flexible, reliable, and cost-effective solution for a range of wireless applications. However, due to the high line-of-sight (LoS) probability, aerial communications between UAVs are vulnerable to eavesdropping attacks, particularly when multiple eavesdroppers collude. In this work, we aim to introduce distributed collaborative beamforming (DCB) into UAV swarms and handle the eavesdropper collusion by controlling the corresponding signal distributions. Specifically, we consider a two-way DCB-enabled aerial communication between two UAV swarms and construct these swarms as two UAV virtual antenna arrays. Then, we minimize the two-way known secrecy capacity and the maximum sidelobe level to avoid information leakage from the known and unknown eavesdroppers, respectively. Simultaneously, we also minimize the energy consumption of UAVs for constructing virtual antenna arrays. Due to the conflicting relationships between secure performance and energy efficiency, we consider these objectives as a multi-objective optimization problem. Following this, we propose an enhanced multi-objective swarm intelligence algorithm via the characterized properties of the problem. Simulation results show that our proposed algorithm can obtain a set of informative solutions and outperform other state-of-the-art baseline algorithms. Experimental tests demonstrate that our method can be deployed in limited computing power platforms of UAVs and is beneficial for saving computational resources.

In this paper, we investigate a self-sensing intelligent reflecting surface (IRS) aided millimeter wave (mmWave) integrated sensing and communication (ISAC) system. Unlike the conventional purely passive IRS, the self-sensing IRS can effectively reduce the path loss of sensing-related links, thus rendering it advantageous in ISAC systems. Aiming to jointly improve the channel estimation (CE) and target/scatterer/user sensing performance in the considered system, we propose a two-phase transmission scheme, where the coarse and refined CE/sensing results are respectively obtained in the first and second phases. Particularly, in each phase, an angle-based sensing turbo variational Bayesian inference (AS-TVBI) algorithm, which combines the VBI, messaging passing and expectation-maximization (EM) methods, is devised to solve the considered joint sensing and CE problem. The proposed algorithm incorporates the partial overlapping structured (POS) sparsity between the sensing and communication channels to improve the performance. Simulation results are provided to verify the superiority of the proposed algorithm.

A new concept of center processing unit (CPU)-integrated simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is proposed, namely computing STAR-RIS. Computation-intensive and delay-sensitive tasks from mobile users can be partially processed at the computing STAR-RIS. We aim to minimize the energy consumption of users and the computing STAR-RIS, and formulate a joint task offloading and transmission resource allocation problem. The solution of this problem is affected by the offloading decision and the amplitude and phase-shift of the computing STAR-RIS. To solve the non-convex problem, we decompose it into two subproblems: 1) For the task offloading subproblem, the offloading decision is optimized utilizing the Karush-Kuhn- Tucker (KKT) conditions; and 2) For the transmission resource allocation subproblem, the transmission-reflection coefficient matrix are optimized via successive convex approximation (SCA). Simulation results show that our proposed algorithm can converge faster and have lower energy consumption than the conventional STAR-RIS.

In this paper, we explore a novel secure and robust beamforming design with a worst-case rate constraint for a multi-user transmit network aided by intelligent reflecting surface (IRS). A bounded channel state information (CSI) error model for all channels is considered. Robust active beamforming at source Alice and the discrete IRS phase shifts are jointly designed to minimize the transmit power at Alice. Applying the$S$-procedure and general sign-definiteness to handle infinite inequalities, an alternative optimization (AO)-based with quantization iterative algorithm is proposed to address the formulated non-convex mixed integer optimization problem. Simulation results demonstrate that our proposed robust design is capable of not only ensuring the quality of service (QoS) for Bobs but also degrading Eve's hearing in comparison to the baselines.

Abstract This paper investigates an intelligent reflecting surface (IRS) assisted downlink short packet transmission system, where an access point sends short packets to multiple devices with the help of an IRS. Specifically, a performance comparison between the frequency division multiple access and time division multiple access is conducted for the considered system, from the perspective of average age of information (AoI). To minimize the maximum average AoI among all devices, the resource allocation and passive beamforming are jointly optimized. However, the formulated problem is difficult to solve due to the non‐convex objective function and coupled variables. Thus, an alternating optimization based algorithm is proposed by exploiting the semidefinite relaxation and bisection search techniques. Simulation results show that time division multiple access can achieve lower AoI by exploiting the time‐selective passive beamforming of IRS for maximizing the signal to noise ratio of each device consecutively. Moreover, it also shows that as the length of information bits becomes sufficiently large as compared to the available bandwidth, the proposed frequency division multiple access transmission scheme becomes more favourable due to more flexible power allocation.

Free space optical (FSO) transmission has emerged as a key candidate technology for 6G to expand new spectrum and improve network capacity due to its advantages of large bandwidth, low-electromagnetic interference, and high-energy efficiency. Resonant beam operating in the infrared band utilizes spatially separated laser cavities to enable safe and mobile high-power energy and high-rate information transmission but is limited by Line-of-Sight (LoS) channel. In this article, we propose a reconfigurable intelligent surface (RIS) assisted resonant beam simultaneous wireless information and power transfer (SWIPT) system and establish an optical field propagation model to analyze the channel state information (CSI), in which LoS obstruction can be detected sensitively and non line-of-sight (NLoS) transmission can be realized by changing the phased of resonant beam in RIS. Numerical results demonstrate that, apart from the transmission distance, the NLoS performance depends on both the horizontal and vertical positions of RIS. The maximum NLoS energy efficiency can achieve 55% within a transfer distance of 10 m, a translation distance of ±4 mm, and rotation angle of ±50°.

In this article, we propose a novel uplink communication system enabled by a transmissive reconfigurable intelligent surface (RIS) transceiver, where orthogonal frequency division multiple access (OFDMA) is applied to multiple users. Specifically, we explore a novel receiver architecture that includes a transmissive RIS and a single horn antenna for reception. Additionally, a channel model based on both planar and spherical waves is developed, accounting for far-field and near-field effects. To achieve the maximum system sum-rate of uplink communications while adhering to Quality-of-Service (QoS) constraints, we propose a joint optimization problem that optimizes power allocation, subcarrier allocation, and transmissive RIS coefficient. However, this problem is nonconvex in view of the strong interdependence among the optimization variables, posing significant challenges for direct solution. Thus, the alternating optimization (AO) algorithm architecture is employed, which decouples optimization variables and divide the problem into two subproblems. The first subproblem focuses on jointly optimizing power allocation and subcarrier allocation, and it is addressed by utilizing the Lagrangian dual decomposition method. Meanwhile, concerning the design of the transmissive RIS coefficient, the second subproblem is tackled by means of the successive convex approximation (SCA) approach. Subsequently, these two subproblems are solved in an alternating manner until the convergence criterion is met. Finally, the numerical results indicate that the proposed algorithm exhibits excellent convergence performance and effectively enhances the system sum-rate compared to other benchmark algorithms.

In this paper, a novel transmissive reconfigurable intelligent surface (TRIS) transceiver empowered integrated sensing and communications (ISAC) system is proposed for future multi-demand terminals. To address interference management, we implement rate-splitting multiple access (RSMA), where the common stream is independently designed for the sensing service. We introduce the sensing quality of service (QoS) criteria based on this structure and construct an optimization problem with the sensing QoS criteria as the objective function to optimize the sensing stream precoding matrix and the communication stream precoding matrix. Due to the coupling of optimization variables, the formulated problem is a non-convex optimization problem that cannot be solved directly. To tackle the above-mentioned challenging problem, alternating optimization (AO) is utilized to decouple the optimization variables. Specifically, the problem is decoupled into three subproblems about the sensing stream precoding matrix, the communication stream precoding matrix, and the auxiliary variables, which is solved alternatively through AO until the convergence is reached. For solving the problem, successive convex approximation (SCA) is applied to deal with the sum-rate threshold constraints on communications, and difference-of-convex (DC) programming is utilized to solve rank-one non-convex constraints. Numerical simulation results verify the superiority of the proposed scheme in terms of improving the communication and sensing QoS.

Cognitive backscatter communication (BackCom) is a promising technology for improving the spectrum- and energy-efficiency of Internet of Things by enabling spectrum sharing and energy saving. However, the performance of cognitive BackCom networks is adversely affected by the mutual interference between the primary and secondary systems and the blocked links caused by obstacles. Additionally, assuming perfect channel state information (CSI) is unrealistic in practical cognitive BackCom networks due to the limited signal processing capabilities of cognitive backscatter nodes (CBNs) and channel delays. To address these challenges, we investigate a robust radio resource allocation and passive beamforming problem for a downlink reconfigurable intelligent surface (RIS)-enhanced cognitive BackCom network under the nonlinear energy-harvesting (EH) model and imperfect CSI. In particular, a primary base station serves multiple primary users (PUs), while multiple pairs of CBNs share the spectrum of PUs to communicate with each other in a harvest-then-transmit way. Our goal is to maximize the total energy efficiency (EE) of CBNs subject to the constraints of maximum interference power, minimum EH, time allocation, and the phase shift of the RIS. To solve the nonconvex optimization problem, we propose an iteration-based EE optimization algorithm that leverages methods of quadratic transform, variable substitution, and semidefinite relaxation. Simulation results verify that the proposed algorithm has improved its EE by 11.39% and reduced outage probabilities by 15% compared to the existing algorithms.

In this article, we investigate the performance of reconfigurable intelligent surface (RIS)-aided spatial shift keying (SSK) wireless communication systems with imperfect channel state information (CSI). Specifically, we study the average bit error probability (ABEP) of two RIS-SSK systems based on intelligent reflection and blind reflection modes. For the intelligent RIS-SSK scheme, we first derive the conditional pairwise error probability of the composite channel through maximum-likelihood (ML) detection. Subsequently, we derive the probability density function of the combined channel. Due to the intricacies of the composite channel formulation, an exact closed-form ABEP expression is unattainable through direct derivation. To this end, we resort to employing the Gaussian–Chebyshev quadrature method to estimate the results. Additionally, we employ$Q$-function approximation to derive the nonexact closed-form expression in the presence of channel estimation errors. For the blind RIS-SSK scheme, we derive both closed-form ABEP expression and asymptotic ABEP expression with imperfect CSI by adopting the ML detector. To offer deeper insights, we explore the impact of discrete reflection phase shifts on the performance of the RIS-SSK system. Finally, we extensively validate all the analytical derivations via Monte Carlo simulations.

This article proposes an intelligent reflecting surface (IRS) assisted integrated sensing and communication (ISAC) system operating in the millimeter-wave band. Specifically, the ISAC system consists of a radar subsystem and a communication subsystem to detect multiple targets and communicate with the users simultaneously. The IRS is used to configure the radio propagation environment by changing the phase of the radio signal to enhance the communication transmission rate. In the proposed scheme, we first derive a closed-form solution for the radar signal covariance matrix to generate a radar beampattern in the angle of interest. Then, we jointly optimize the beamforming vector of the communication subsystem and the IRS phase shifts to enhance the communication transmission rate. To decouple the multiple variables to be optimized, the alternating optimization and quadratic transformation methods are applied to determine the communication beamforming vector and the IRS phase shifts. Specifically, we utilize the majorization minimization and the complex circle manifold methods to compute the IRS phase shifts. Simulation results verify the effectiveness of the proposed algorithm and demonstrate that an IRS can improve the performance of ISAC systems.

Low Earth Orbit (LEO) satellites have emerged as crucial enablers of direct connections with remote terrestrial terminals. However, energy limitations and insufficient antenna capabilities at the terminals often hamper these connections, resulting in inefficient communications and frequent ping-pong handovers. This paper proposes a Distributed Collaborative Beamforming (DCB)-based uplink communication paradigm for enabling ground-space direct communications. Specifically, DCB treats the terminals that are unable to establish efficient direct connections with the LEO satellites as distributed antennas, forming a virtual antenna array to enhance the terminal-to-satellite uplink achievable rates and durations. However, such systems need multiple trade-off policies that jointly balance the terminal-satellite uplink achievable rate, energy consumption of terminals, and satellite switching frequency to satisfy the scenario requirement changes. Thus, we formulate a long-term multi-objective optimization problem to optimize these goals simultaneously. To address availability in different terminal cluster scales, we reformulate this problem into an action space-reduced and universal Multi-Objective Markov Decision Process (MOMDP). Then, we propose an Evolutionary Multi-Objective Deep Reinforcement Learning (EMODRL) algorithm to obtain multiple policies, in which the low-value actions are masked to speed up the training process. Simulation results show that DCB enables terminals that cannot reach the uplink achievable rate threshold to achieve efficient direct uplink transmission. Moreover, the proposed algorithm outmatches various baselines and saves 30% handover frequency with a similar uplink achievable rate compared with the rate greedy method, which thus reveals that the proposed method is an effective solution for enabling direct ground-space communications.

Intelligent surfaces (ISs) have emerged as a key technology to empower a wide range of appealing applications for wireless networks, due to their low cost, high energy efficiency, flexibility of deployment, and capability of constructing favorable wireless channels/radio environments. Moreover, the recent advent of several new IS architectures further expanded their electromagnetic functionalities from passive reflection to active amplification, simultaneous reflection, and refraction, as well as holographic beamforming. However, the research on ISs is still in rapid progress and there have been recent technological advances in ISs and their emerging applications that are worthy of a timely review. Thus, in this article, we provide a comprehensive survey on the recent development and advances of ISs-aided wireless networks. Specifically, we start with an overview on the anticipated use cases of ISs in future wireless networks such as 6G, followed by a summary of the recent standardization activities related to ISs. Then, the main design issues of the commonly adopted reflection-based IS and their state-of-the-art solutions are presented in detail, including reflection optimization, deployment, signal modulation, wireless sensing, and integrated sensing and communications. Finally, recent progress and new challenges in advanced IS architectures are discussed to inspire future research.

Movable antenna (MA) array is a novel technology recently developed where positions of transmit/receive antennas can be flexibly adjusted in the specified region to reconfigure the wireless channel and achieve a higher capacity. In this letter, we, for the first time, investigate the MA array-assisted physical-layer security where the confidential information is transmitted from a MA array-enabled Alice to a single-antenna Bob, in the presence of multiple single-antenna and colluding eavesdroppers. We aim to maximize the achievable secrecy rate by jointly designing the transmit beamforming and positions of all antennas at Alice subject to the transmit power budget and specified regions for positions of all transmit antennas. The resulting problem is highly non-convex, for which the projected gradient ascent (PGA) and the alternating optimization methods are utilized to obtain a high-quality suboptimal solution. Simulation results demonstrate that since the additional spatial degree of freedom (DoF) can be fully exploited, the MA array significantly enhances the secrecy rate compared to the conventional fixed-position antenna (FPA) array.

In this paper, we consider an intelligent reflecting surface (IRS) assisted downlink multiuser communication system with improper Gaussian signaling (IGS) that serves as generalized Gaussian signaling and can effectively combat multiuser interference. We focus on the max-min achievable rate optimization problem by jointly optimizing the transmit beamforming vectors and reflecting phase shifts, subject to the transmit power budget constraint at the access point (AP). We propose a low-complexity iterative algorithm based on a two-layer iterative procedure, which differs from these existing algorithms that rely on inefficient alternating optimization framework and high computational complexity convex optimization tools. Specifically, in the outer layer procedure, we employ a tractable lower bound of user communication rate to reformulate the original problem and repeatedly update the lower bound in each iteration. In the inner layer procedure, based on the alternating direction method of multipliers (ADMM), we decompose the reformulated problem into several convex sub-problems, which can be alternately solved by closed-form solutions. Furthermore, we study the initialization, convergence, and computational complexity of the proposed algorithm. Additionally, we simplify the algorithm to make it applicable for the cases of conventional proper Gaussian signaling (PGS) and without IRS. Finally, numerical results validate the advantages of the proposed algorithm over benchmarking algorithm in terms of rate performance and average execution time.

Autonomous driving and intelligent transportation applications have dramatically increased the demand for high-accuracy and low-latency localization services. While cellular networks are potentially capable of target detection and localization, achieving accurate and reliable positioning faces critical challenges. Particularly, the relatively small radar cross sections (RCS) of moving targets and the high complexity for measurement association give rise to weak echo signals and discrepancies in the measurements. To tackle this issue, we propose a novel approach for multi-target localization by leveraging the controllable signal reflection capabilities of intelligent reflecting surfaces (IRSs). Specifically, IRSs are strategically mounted on the targets (e.g., vehicles and robots), enabling effective association of multiple measurements and facilitating the localization process. We aim to minimize the maximum Cramér-Rao lower bound (CRLB) of targets by jointly optimizing the target association, the IRS phase shifts, and the dwell time. However, solving this CRLB optimization problem is non-trivial due to the non-convex objective function and closely coupled variables. For single-target localization, a simplified closed-form expression is presented for the case where base stations (BSs) can be deployed flexibly, and the optimal BS location is derived to provide a lower performance bound of the original problem. Then, we prove that the transformed problem is a monotonic optimization, which can be optimally solved by the Polyblock-based algorithm. Moreover, based on derived insights for the single-target case, we propose a heuristic algorithm to optimize the target association and time allocation for the multi-target case. Furthermore, we provide useful guidance for the practical implementation of the proposed localization scheme by theoretically analyzing the relationship between time slots, BSs, and targets. Simulation results verify that deploying IRS on vehicles and effective phase shift design can effectively improve the resolution ability of multi-vehicle positioning and reduce the requirements of the number of BSs.

Intelligent reflecting surface (IRS) has garnered growing interest and attention due to its potential for facilitating and supporting wireless communications and sensing. This paper studies a semi-passive IRS-enabled sensing system, where an IRS consists of both passive reflecting elements and active sensors. Our goal is to minimize the Cramér-Rao bound (CRB) for parameter estimation under both point and extended target cases. Towards this goal, we begin by deriving the CRB for the direction-of-arrival (DoA) estimation in closed-form and then theoretically analyze the IRS reflecting elements and sensors allocation design based on the CRB under the point target case with a single-antenna base station (BS). To efficiently solve the corresponding optimization problem for the case with a multi-antenna BS, we propose an efficient algorithm by jointly optimizing the IRS phase shifts and the BS beamformers. Under the extended target case, the CRB for the target response matrix (TRM) estimation is minimized via the optimization of the BS transmit beamformers. Moreover, we explore the influence of various system parameters on the CRB and compare these effects to those observed under the point target case. Simulation results show the effectiveness of the semi-passive IRS and our proposed beamforming design for improving the performance of the sensing system.

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

This paper studies the fair transmission design for an intelligent reflecting surface (IRS) aided rate-splitting multiple access (RSMA). IRS is used to establish a good signal propagation environment and enhance the RSMA transmission performance. The fair rate adaption problem is constructed as a max-min optimization problem. To solve the optimization problem, we adopt an alternative optimization (AO) algorithm to optimize the power allocation, beamforming, and decoding order, respectively. A generalized power iteration (GPI) method is proposed to optimize the receive beamforming, which can improve the minimum rate of devices and reduce the optimization complexity. At the base station (BS), a successive group decoding (SGD) algorithm is proposed to tackle the uplink signal estimation, which trades off the fairness and complexity of decoding. At the same time, we also consider robust communication with imperfect channel state information at the transmitter (CSIT), which studies robust optimization by using lower bound expressions on the expected data rates. Extensive numerical results show that the proposed optimization algorithm can significantly improve the performance of fairness. It also provides reliable results for uplink communication with imperfect CSIT.

Drawing inspiration from the advantages of intelligent reflecting surfaces (IRS) in wireless networks, this paper presents a novel design for intelligent omni surface (IOS) enabled integrated sensing and communications (ISAC). By harnessing the power of multi-antennas and a multitude of elements, the dual-function base station (BS) and IOS collaborate to realize joint active and passive beamforming, enabling seamless 360-degree ISAC coverage. The objective is to maximize the minimum signal-to-interference-plus-noise ratio (SINR) of multi-target sensing while ensuring the multi-user multi-stream communications. To achieve this, a comprehensive optimization approach is employed, encompassing the design of radar receive vector, transmit beamforming matrix, and IOS transmissive and reflective coefficients. Due to the non-convex nature of the formulated problem, an auxiliary variable is introduced to transform it into a more tractable form. Consequently, the problem is decomposed into three sub-problems based on the block coordinate descent algorithm. Semidefinite relaxation and successive convex approximation methods are leveraged to convert the sub-problem into a convex problem, while the iterative rank minimization algorithm and penalty function method ensure the equivalence. Furthermore, the scenario is extended to mode switching and time switching protocols. Simulation results validate the convergence and superior performance of the proposed algorithm compared to other benchmark algorithms.

Reconfigurable intelligent surface (RIS)-assisted index modulation system schemes are considered to be a promising technology for sixth-generation (6G) wireless communication systems, which can enhance various system capabilities such as coverage and reliability. However, obtaining perfect channel state information (CSI) is challenging due to the lack of a radio frequency chain in RIS. In this paper, we investigate the RIS-assisted full-duplex (FD) two-way space shift keying (SSK) system under imperfect CSI, where the signal emissions are augmented by deploying RISs in the vicinity of two FD users. The maximum likelihood detector is utilized to recover the transmit antenna index. With this in mind, we derive closed-form average bit error probability (ABEP) expression based on the Gaussian-Chebyshev quadrature (GCQ) method, and provide the upper bound and asymptotic ABEP expressions in the presence of channel estimation errors. To gain more insights, we also derive the outage probability and provide the throughput of the proposed scheme with imperfect CSI. The correctness of the analytical derivation results is confirmed via Monte Carlo simulations. It is demonstrated that increasing the number of elements of RIS can significantly improve the ABEP performance of the FD system over the half-duplex (HD) system. Furthermore, in the high SNR region, the ABEP performance of the FD system is better than that of the HD system.

This paper considers an unmanned aerial vehicle (UAV)-enabled multi-package delivery system, where a cargo UAV collects the parcels of ground users, and finally delivers them to the destination. One key aspect of this system is to ensure a stable and reliable connection between the UAV and the base station (BS) throughout the mission for the safety of the UAV flight. To this end, we minimize the communication outage time between the UAV and the BSs while maximizing the value of the packages picked up via optimizing the UAV path and pick-up design. Although the formulated problem is difficult to solve due to its non-convexity, we propose a connectivity-aware delivery (CAD) framework that divides the delivery mission into the path design phase and the pick-up design phase to address this challenging problem. Specifically, in the path design phase, we design the optimal flight path between any two package collection points of the UAV based on deep reinforcement learning to reduce the expected communication outage duration. In the pick-up design phase, we propose a genetic algorithm based pick-up algorithm which decides the selection and order of the packages to be picked by the UAV to maximize the value of the picked-up parcels under the constraints of the UAV’s load and energy. Extensive experiments and comparative studies demonstrate the superior performance of our framework in terms of both the outage rate and total value of the picked packages.

This paper investigates an aerial reconfigurable intelligent surface (RIS)-aided communication system under the probabilistic line-of-sight (LoS) channel, where an unmanned aerial vehicle (UAV) equipped with an RIS is deployed to assist two ground nodes in their information exchange. An optimization problem with the objective of maximizing the minimum average achievable rate is formulated to jointly design the communication scheduling, the RIS’s phase shift, and the three-dimensional (3D) UAV trajectory. To solve such a non-convex problem, we propose an efficient iterative algorithm to obtain its suboptimal solution. Simulation results show that our proposed design significantly outperforms the existing schemes and provides new insights into the elevation angle and distance trade-off for the UAV-borne RIS communication system.

With the emergence of communication services with stringent requirements such as autonomous driving or on-flight Internet, the sixth-generation (6G) wireless network is envisaged to become an enabling technology for future transportation systems. In this paper, two ways of interactions between 6G networks and transportation are extensively investigated. On one hand, the new usage scenarios and capabilities of 6G over existing cellular networks are firstly highlighted. Then, its potential in seamless and ubiquitous connectivity across the heterogeneous space-air-ground transportation systems is demonstrated, where railways, airplanes, high-altitude platforms and satellites are investigated. On the other hand, we reveal that the introduction of 6G guarantees a more intelligent, efficient and secure transportation system. Specifically, technical analysis on how 6G can empower future transportation is provided, based on the latest research and standardization progresses in localization, integrated sensing and communications, and security. The technical challenges and insights for a road ahead are also summarized for possible inspirations on 6G enabled advanced transportation.

Wireless information surveillance has received widespread attention due to the urgency of monitoring growing suspicious communications. This paper considers a challenging surveillance scenario, where the monitor (E) intends to eavesdrop the suspicious multihop communications from a long distance to ensure concealment, leading to the eavesdropping condition undesirable. To tackle this challenging, we propose a novel simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted surveillance strategy, where the STAR-RIS, acts as a “bridge”, is deliberately deployed between the suspicious system and E, to adaptively transmit and reflect the suspicious signal and E's jamming signal, and then facilitate E's eavesdropping. Specifically, we consider the adaptive rate transmission and the delay-limited transmission for the suspicious system, and accordingly maximize E's instantaneous and average eavesdropping rate, by jointly optimizing the passive transmission- and reflection-coefficient matrices at the STAR-RIS, the jamming set and jamming power allocations of E (across all hops). The optimization problems in both transmission modes include numerous integer and continuous variables and thus are highly non-convex. Nevertheless, we show by detailed analysis that the original problem in each mode can be solved by only considering two possible cases, where E and the STAR-RIS intend to enhance and reduce the suspicious transmission rate, respectively. More importantly, in each case, many of necessary prerequisites for achieving the optimal solution are first determined analytically. Armed with these, the optimization problem then can be solved by leveraging the successive convex approximation technique and the simple search. As demonstrated by simulation results, since our proposed strategy is adaptive in term of varying the suspicious transmission rate, it will achieve significant eavesdropping performance gain as compared to other competitive benchmarks.

Movable antenna (MA) technology is highly promising for improving communication performance, due to its advantage of flexibly adjusting positions of antennas to reconfigure channel conditions. In this paper, we investigate MAs-assisted secure transmission under a legitimate transmitter Alice, a legitimate receiver Bob and multiple eavesdroppers. Specifically, we consider a practical scenario where Alice has no any knowledge about the instantaneous non-line-of-sight component of the wiretap channel. Under this setup, we evaluate the secrecy performance by adopting the secrecy outage probability metric, the tight approximation of which is first derived by interpreting the Rician fading as a special case of Nakagami fading and concurrently exploiting the Laguerre series approximation. Then, we minimize the secrecy outage probability by jointly optimizing the transmit beamforming and positions of antennas at Alice. However, the problem is highly non-convex because the objective includes the complex incomplete gamma function. To tackle this challenge, we, for the first time, effectively approximate the inverse of the incomplete gamma function as a simple linear model. Based on this approximation, we arrive at a simplified problem with a clear structure, which can be solved via the developed alternating projected gradient ascent (APGA) algorithm. Considering the high complexity of the APGA, we further design another scheme where the zero-forcing based beamforming is adopted by Alice, and then we transform the problem into minimizing a simple function which is only related to positions of antennas at Alice. Such problem is well-solved via another projected gradient descent algorithm developed with a lower complexity. As demonstrated by simulations, our proposed schemes achieve significant performance gains compared to conventional schemes based on fixed-position antennas.

Unmanned aerial vehicle (UAV) network is a promising technology for assisting Internet-of-Things (IoT), where a UAV can use its limited service coverage to harvest and disseminate data from IoT devices with low transmission abilities. The existing UAV-assisted data harvesting and dissemination schemes largely require UAVs to frequently fly between the IoTs and access points, resulting in extra energy and time costs. To reduce both energy and time costs, a key way is to enhance the transmission performance of IoT and UAVs. In this work, we introduce collaborative beamforming into IoTs and UAVs simultaneously to achieve energy and time-efficient data harvesting and dissemination from multiple IoT clusters to remote base stations (BSs). Except for reducing these costs, another non-ignorable threat lies in the existence of the potential eavesdroppers, whereas the handling of eavesdroppers often increases the energy and time costs, resulting in a conflict with the minimization of the costs. Moreover, the importance of these goals may vary relatively in different applications. Thus, we formulate a multi-objective optimization problem (MOP) to simultaneously minimize the mission completion time, signal strength towards the eavesdropper, and total energy cost of the UAVs. We prove that the formulated MOP is an NP-hard, mixed-variable optimization, and large-scale optimization problem. Thus, we propose a swarm intelligence-based algorithm to find a set of candidate solutions with different trade-offs which can meet various requirements in a low computational complexity. We also show that swarm intelligence methods need to enhance solution initialization, solution update, and algorithm parameter update phases when dealing with mixed-variable optimization and large-scale problems. Simulation results demonstrate the proposed algorithm outperforms state-of-the-art swarm intelligence algorithms and also show that the proposed method can reduce time and energy costs significantly compared with the benchmark strategies based on multi-hop and long-range flight.

In this paper, we investigate an unmanned aerial vehicle (UAV)-assistant air-to-ground communication system, where multiple UAVs form a UAV-enabled virtual antenna array (UVAA) to communicate with remote base stations by utilizing collaborative beamforming. To improve the work efficiency of the UVAA, we formulate a UAV-enabled collaborative beamforming multi-objective optimization problem (UCBMOP) to simultaneously maximize the transmission rate of the UVAA and minimize the energy consumption of all UAVs by optimizing the positions and excitation current weights of all UAVs. This problem is challenging because these two optimization objectives conflict with each other, and they are non-concave to the optimization variables. Moreover, the system is dynamic, and the cooperation among UAVs is complex, making traditional methods take much time to compute the optimization solution for a single task. In addition, as the task changes, the previously obtained solution will become obsolete and invalid. To handle these issues, we leverage the multi-agent deep reinforcement learning (MADRL) to address the UCBMOP. Specifically, we use the heterogeneous-agent trust region policy optimization (HATRPO) as the basic framework, and then propose an improved HATRPO algorithm, namely HATRPO-UCB, where three techniques are introduced to enhance the performance. Simulation results demonstrate that the proposed algorithm can learn a better strategy compared with other methods. Moreover, extensive experiments also demonstrate the effectiveness of the proposed techniques.

Unmanned aerial vehicles (UAVs) are playing an increasingly important role in assisting fast-response post-disaster rescue due to their fast deployment, flexible mobility, and low cost. However, UAVs face the challenges of limited battery capacity and computing resources, which could shorten the expected flight endurance of UAVs and increase the rescue response delay during performing mission-critical tasks. To address these challenges, we first present a three-layer post-disaster rescue computing architecture by leveraging the aerial-terrestrial edge capabilities of mobile edge computing (MEC) and vehicle fog computing (VFC), which consists of a vehicle fog layer, a UAV client layer, and a UAV edge layer. Moreover, we formulate a joint task offloading and resource allocation optimization problem (JTRAOP) with the aim of maximizing the time-average system utility. Since the formulated JTRAOP is proved to be NP-hard, we propose an MEC-VFC-aided task offloading and resource allocation (MVTORA) approach, which consists of a game theoretic algorithm for task offloading decision, a convex optimization-based algorithm for MEC resource allocation, and an evolutionary computation-based hybrid algorithm for VFC resource allocation. Simulation results validate that the proposed approach can achieve superior system performance compared to alternative approaches, especially under heavy system workloads.

Recent developments in unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) have provided users with flexible and resilient computing services. However, meeting the computation-intensive and delay-sensitive demands of users poses a significant challenge due to the limited resources of UAVs. To address this challenge, we consider a multi-UAV-assisted MEC system. Based on this system, we formulate a multi-objective optimization problem aiming at minimizing the total task completion delay, reducing the total UAV energy consumption, and maximizing the total number of offloaded tasks. Since the problem is a mixed-integer non-linear programming (MINLP) and NP-hard problem, we propose a joint task offloading, computation resource allocation, and UAV trajectory control (JTORATC) approach. The problem is split into three components to cope with the coupling of these decision variables, and then solved individually to obtain the corresponding decisions. Specifically, the sub-problem of task offloading is solved by using distributed splitting and threshold rounding methods, the sub-problem of computation resource allocation is solved by adopting the Karush-Kuhn-Tucker (KKT) method, and the sub-problem of UAV trajectory control is solved by employing the successive convex approximation (SCA) method. Simulation results show that the proposed JTORATC has superior performance compared with the other benchmark methods.

Unmanned aerial vehicles (UAVs) are playing a pivotal role in wireless networks due to their high mobility and on-demand deployment advantages. However, the UAV-enabled communications are susceptible to be wiretapped by eavesdroppers due to the strong line-of-sight (LoS) dominated air-ground channel. In this paper, we consider a UAV-enabled secure communication scenario, in which a group of UAVs form a UAV-enabled virtual antenna array (UVAA) to transmit information towards the remote base stations (BSs) via collaborative beamforming (CB), while multiple known and unknown eavesdroppers aiming to wiretap the information. Specifically, a secure communication multi-objective optimization problem (SCMOP) is formulated to achieve the maximization of the worst-case secrecy rate, the minimization of the maximum sidelobe level (SLL) as well as the minimization of the flight energy consumption of UAVs by obtaining optimal locations and excitation current weights concerning the UAVs as well as determining an optimal receiver BS that can achieve superior communication performance. To solve the formulated SCMOP which is demonstrated to be non-convex and NP-hard, an improved multi-objective salp swarm algorithm (IMSSA) with several specific operating factors is proposed. Simulations results demonstrate that the proposed IMSSA can deal with the formulated SCMOP effectively and outperforms other benchmark strategies. Moreover, the multi-hop relay is introduced to verify the reasonability of the UVAA system, and two benchmark schemes of the formulated SCMOP are introduced to demonstrate the necessity of the formulated SCMOP. In addition, the performance of the UVAA system under certain unexpected circumstances is estimated. Finally, experimental implementation is conducted by using a Raspberry Pi and the results demonstrate the practicality of the proposed CB-based secure communication approach in real-world scenarios.

Unmanned aerial vehicles (UAVs) as aerial relays are practically appealing for assisting the Internet of Things (IoT) network. In this work, we aim to utilize a UAV swarm to assist the secure communication between the micro base station (MBS) equipped with the planar antenna array (PAA) and the IoT terminal devices by collaborative beamforming (CB), so as to counteract the effects of the eavesdropper colluding in the time domain. Specifically, we formulate a UAV swarm-enabled secure relay multi-objective optimization problem (US*****RMOP) for simultaneously maximizing the achievable sum rate of the associated IoT terminal devices, minimizing the achievable sum rate of the eavesdropper and minimizing the energy consumption of UAV swarm, by jointly optimizing the excitation current weights of both MBS and UAV swarm, the selection of the UAV receiver, the position of UAVs and user association order of IoT terminal devices. Furthermore, the formulated US*****RMOP is proved to be a non-convex, NP-hard and large-scale optimization problem. Therefore, we propose an improved multi-objective grasshopper algorithm (IMOGOA) with some specific designs to address the problem. Simulation results exhibit the effectiveness of the proposed UAV swarm-enabled collaborative secure relay strategy and demonstrate the superiority of IMOGOA.

Intelligent reflecting surfaces (IRSs) have recently attained growing popularity in wireless networks owning to their capability to customize the wireless channel via smartly configured passive reflections. In addition to optimizing IRS reflection patterns, the flexible deployment of IRSs offers another design degree of freedom (DoF) to reconfigure the wireless propagation environment in favour of signal transmission. To unveil the impact of IRS deployment on the system capacity, we investigate the capacity of a broadcast channel with a multi-antenna base station (BS) sending independent messages to multiple users, aided by IRSs with N elements. In particular, both the distributed and centralized IRS deployment architectures are considered. Regarding the distributed IRS, the N IRS elements form multiple IRSs and each of them is installed near a user cluster; while for the centralized IRS, all IRS elements are located in the vicinity of the BS. To draw essential insights, we first derive the maximum capacity achieved by the distributed IRS and centralized IRS, respectively, under the assumption of line-of-sight (LoS) propagation and homogeneous channel setups. By carefully capturing the fundamental tradeoff between the spatial multiplexing gain and passive beamforming gain, we rigourously prove that the capacity of the distributed IRS is higher than that of the centralized IRS provided that the total number of IRS elements is above a threshold. Motivated by the superiority of the distributed IRS, we then focus on the transmission and element allocation design under the distributed IRS. By exploiting the user channel correlation of intra-clusters and inter-clusters, an efficient hybrid multiple access scheme relying on both spatial and time domains is proposed to fully exploit both the passive beamforming gain and spatial DoF. Moreover, the IRS element allocation problem is investigated for the objectives of the sum-rate maximization and the minimum user rate maximization, respectively. Finally, extensive numerical results are provided to validate our theoretical finding and also to unveil the effectiveness of the distributed IRS for improving the system capacity under various system setups.

In this paper, we investigate a self-sensing intelligent reflecting surface (IRS) aided millimeter wave (mmWave) integrated sensing and communication (ISAC) system. Unlike the conventional purely passive IRS, the self-sensing IRS can effectively reduce the path loss of sensing-related links, thus rendering it advantageous in ISAC systems. Aiming to jointly sense the target/scatterer/user positions as well as estimate the sensing and communication (SAC) channels in the considered system, we propose a two-phase transmission scheme, where the coarse and refined sensing/channel estimation (CE) results are respectively obtained in the first phase (using scanning-based IRS reflection coefficients) and second phase (using optimized IRS reflection coefficients). For each phase, an angle-based sensing turbo variational Bayesian inference (AS-TVBI) algorithm, which combines the VBI, messaging passing and expectation-maximization (EM) methods, is developed to solve the considered joint location sensing and CE problem. The proposed algorithm effectively exploits the partial overlapping structured (POS) sparsity and 2-dimensional (2D) block sparsity inherent in the SAC channels to enhance the overall performance. Based on the estimation results from the first phase, we formulate a Cramér-Rao bound (CRB) minimization problem for optimizing IRS reflection coefficients, and through proper reformulations, a low-complexity manifold-based optimization algorithm is proposed to solve this problem. Simulation results are provided to verify the superiority of the proposed transmission scheme and associated algorithms.

Movable antenna (MA) is an emerging technology that utilizes localized antenna movement to achieve better channel conditions for enhancing communication performance. In this paper, we study the MA-enhanced multicast transmission from a base station equipped with multiple MAs to multiple groups of single-MA users. Our goal is to maximize the minimum weighted signal-to-interference-plus-noise ratio (SINR) among all the users by jointly optimizing the position of each transmit/receive MA and the transmit beamforming. To tackle this challenging problem, we first consider the single-group scenario and propose an efficient algorithm based on the techniques of alternating optimization and successive convex approximation. Particularly, when optimizing transmit or receive MA positions, we construct a concave lower bound for the signal-to-noise ratio (SNR) of each user using only the second-order Taylor expansion, which simplifies the problem-solving process compared to the existing two-step approximation method. The proposed design is then extended to the general multi-group scenario. Simulation results show that the proposed algorithm converges faster than the existing two-step approximation method, achieving a 3.4% enhancement in max-min SNR. Moreover, it can improve the max-min SNR/SINR by up to 22.5%, 181.7%, and 343.9% compared to benchmarks employing only receive MAs, only transmit MAs, and both transmit and receive FPAs, respectively.

This paper studies an intelligent reflecting surface (IRS)-aided multi-antenna simultaneous wireless information and power transfer (SWIPT) system where anM-antenna access point (AP) servesKsingle-antenna information users (IUs) andJsingle-antenna energy users (EUs) with the aid of an IRS with phase errors. We explicitly concentrate on overloaded scenarios whereK+J>MandK≥M. Our goal is to maximize the minimum throughput among all the IUs by optimizing the allocation of resources (including time, transmit beamforming at the AP, and reflect beamforming at the IRS), while guaranteeing the minimum amount of harvested energy at each EU. Towards this goal, we propose two user grouping (UG) schemes, namely, the non-overlapping UG scheme and the overlapping UG scheme, where the difference lies in whether identical IUs can exist in multiple groups. Different IU groups are served in orthogonal time dimensions, while the IUs in the same group are served simultaneously with all the EUs via spatial multiplexing. The two problems corresponding to the two UG schemes are mixed-integer non-convex optimization problems and difficult to solve optimally. We first provide a method to check the feasibility of these two problems, and then propose efficient algorithms for them based on the big-M formulation, the penalty method, the block coordinate descent, and the successive convex approximation. Simulation results show that: 1) the non-robust counterparts of the proposed robust designs are unsuitable for practical IRS-aided SWIPT systems with phase errors since the energy harvesting constraints cannot be satisfied; 2) the proposed UG strategies can significantly improve the max-min throughput over the benchmark schemes without UG or adopting random UG; 3) the overlapping UG scheme performs much better than its non-overlapping counterpart when the absolute difference betweenKandMis small and the EH constraints are not stringent.

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

Integrated sensing and communication (ISAC) capability is envisioned as one key feature for future cellular networks. Classical half-duplex (HD) radar sensing is conducted in a “first-emit-then-listen” manner. One challenge to realize HD ISAC lies in the discrepancy of the two systems’ time scheduling for transmitting and receiving. This difficulty can be overcome by full-duplex (FD) transceivers. Besides, ISAC generally has to comprise its communication rate due to realizing sensing functionality. This loss can be compensated by the emerging reconfigurable intelligent surface (RIS) technology. This paper considers the joint design of beamforming, power allocation and signal processing in a FD uplink communication system aided by RIS, which is a highly nonconvex problem. To resolve this challenge, via leveraging the cutting-the-edge majorization-minimization (MM) and penalty-dual-decomposition (PDD) methods, we develop an iterative solution that optimizes all variables via using convex optimization techniques. Besides, by wisely exploiting alternative direction method of multipliers (ADMM) and optimality analysis, we further develop a low complexity solution that updates all variables analytically and runs highly efficiently. Numerical results are provided to verify the effectiveness and efficiency of our proposed algorithms and demonstrate the significant performance boosting by employing RIS in the FD ISAC system.

In this paper, an intelligent reflecting surface (IRS) is leveraged to enhance the physical layer security of an integrated sensing and communication (ISAC) system in which the IRS is deployed to not only assist the downlink communication for multiple users, but also create a virtual line-of-sight (LoS) link for target sensing. In particular, we consider a challenging scenario where the target may be a suspicious eavesdropper that potentially intercepts the communication-user information transmitted by the base station (BS). To ensure the sensing quality while preventing the eavesdropping, dedicated sensing signals are transmitted by the BS. We investigate the joint design of the phase shifts at the IRS and the communication as well as radar beamformers at the BS to maximize the sensing beampattern gain towards the target, subject to the maximum information leakage to the eavesdropping target and the minimum signal-to-interference-plus-noise ratio (SINR) required by users. Based on the availability of perfect channel state information (CSI) of all involved user links and the potential target location of interest at the BS, two scenarios are considered and two different optimization algorithms are proposed. For the ideal scenario where the CSI of the user links and the potential target location are perfectly known at the BS, a penalty-based algorithm is proposed to obtain a high-quality solution. In particular, the beamformers are obtained with a semi-closed-form solution using Lagrange duality and the IRS phase shifts are solved for in closed form by applying the majorization-minimization (MM) method. On the other hand, for the more practical scenario where the CSI is imperfect and the potential target location is uncertain in a region of interest, a robust algorithm based on the$\cal S$-procedure and sign-definiteness approaches is proposed. Simulation results demonstrate the effectiveness of the proposed scheme in achieving a trade-off between the communication quality and the sensing quality, and also show the tremendous potential of IRS for use in sensing and improving the security of ISAC systems.

This paper studies a communication-centric integrated sensing and communication (ISAC) system, where a multi-antenna base station (BS) simultaneously performs downlink communication and target detection. A novel target detection and information transmission protocol is proposed, where the BS executes the channel estimation and beamforming successively and meanwhile jointly exploits the pilot sequences in the channel estimation stage and user information in the transmission stage to assist target detection. We investigate the joint design of the pilot matrix, training duration, and transmit beamforming to maximize the probability of target detection, subject to the minimum achievable rate required by the user. However, designing the optimal pilot matrix is rather challenging since there is no closed-form expression of the detection probability with respect to the pilot matrix. To tackle this difficulty, we resort to designing the pilot matrix based on the information-theoretic criterion to maximize the mutual information (MI) between the received observations and BS-target channel coefficients for target detection. We first derive the optimal pilot matrix for both channel estimation and target detection, and then propose a unified pilot matrix structure to balance minimizing the channel estimation error (MSE) and maximizing MI. Based on the proposed structure, a low-complexity successive refinement algorithm is proposed. In addition, we rigorously analyze the impact of pilot length and pilot matrix on two fundamental tradeoffs, namely MSE-MI and Rate-MI. Simulation results demonstrate that the proposed pilot matrix structure can well balance the MSE-MI and the Rate-MI tradeoffs, and show the significant region improvement of our proposed design as compared to other benchmark schemes. Furthermore, it is unveiled that as the communication channel is more spatially correlated, the Rate-MI region can be further enlarged.

In this paper, we propose a novel UAV aided ground nodes (GNs) localization and communication integrated framework, where the age of information (AoI) is introduced to evaluate the system timeliness. We aim to jointly optimize the UAV trajectory, localization accuracy, bandwidth and beamwidth, to guarantee the information freshness. Specifically, we give a two-stage method, where a low complexity initial UAV trajectory searching algorithm is firstly proposed, based on theroughposition information of GNs. Afterwards, we formulate a joint UAV location and resource optimization problem. This essential mixed integer problem can be solved by efficient successive convex approximation based iterative algorithm. Simulations show that the localization and communication integrated framework can obtain about 50% performance gain compared with the UAV communication aid only solution, and over 37% performance gain via proper resource allocation. Moreover, the analysis reveals that our proposed scheme strikes a balance among the time durations of localization, data transmission and UAV movement. Additionally, we conduct practical experiments to draw valuable insights into the system design and implementations (An experimental can be found on the supplementary materials, or online at https://youtu.be/OX6Bgz6naUA).

In intelligent reflecting surface (IRS) assisted communication, beam search is usually time-consuming as the multiple-input multiple-output (MIMO) of IRS is usually very large. The hierarchical codebook is a widely accepted method for reducing the complexity of searching time. The performance of this method strongly depends on the design scheme of beamforming of different beamwidths. In this paper, a non-constant phase difference (NCPD) beamforming algorithm is proposed. To implement the NCPD algorithm, we first model the phase shift of IRS as a continuous function and then determine the parameters of the continuous function through the analysis of its array factor. Then, we propose a hierarchical codebook and two beam training schemes, namely the joint searching (JS) scheme and direction-wise searching (DWS) scheme by using the NCPD algorithm which can flexibly change the width, direction, and shape of the beam formed by the IRS array. Numerical results show that the NCPD algorithm is more accurate with smaller side lobes, and also more stable on IRS of different sizes compared to other wide beam algorithms. The misalignment rate of the beam formed by the NCPD method is significantly reduced. The time complexity of the NCPD algorithm is constant, thus making it more suitable for solving the beamforming design problem with practically large IRS.

This paper investigates a new self-sustainable intelligent omni-surface (S-IOS) aided multi-user wireless network, where the S-IOS harvests the radio frequency energy from the signals transmitted by the access point (AP) and exploits the harvested energy to provide full-dimensional beamforming services for the users. Three efficient operating protocols for the S-IOS, namely time switching, power splitting, and mode switching, are proposed to enable the dual-functionality of energy harvesting and information transmission. For each protocol, we design a joint optimization framework of transmit beamforming at the AP, refraction/reflection beamforming at the S-IOS, and energy harvesting schedule at the S-IOS, to maximize the network sum rate. Despite the challenging non-convex optimization problems with highly coupled and/or integer optimization variables, we develop computationally-efficient algorithms to solve them in an iterative manner, which exploit the intrinsic structure of the problems and employ the penalty-based method and the successive convex approximation. Numerical results confirm the efficiency of our developed optimization algorithms, demonstrate the significant importance of the S-IOS for spectral and energy efficient wireless communications, and quantify the performance advantage of the proposed designs over the baseline schemes.

We consider unmanned aerial vehicle (UAV)-enabled wireless systems where downlink communications between a multi-antenna UAV and multiple users are assisted by a hybrid active-passive reconfigurable intelligent surface (RIS). We aim at a fairness design of two typical UAV-enabled networks, namely the static-UAV network where the UAV is deployed at a fixed location to serve all users at the same time, and the mobile-UAV network which employs the time division multiple access protocol. In both networks, our goal is to maximize the minimum rate among users through jointly optimizing the UAV’s location/trajectory, transmit beamformer, and RIS coefficients. The resulting problems are highly nonconvex due to a strong coupling between the involved variables. We develop efficient algorithms based on block coordinate ascend and successive convex approximation to effectively solve these problems in an iterative manner. In particular, in the optimization of the mobile-UAV network, closed-form solutions to the transmit beamformer and RIS passive coefficients are derived. Numerical results show that a hybrid RIS equipped with only 4 active elements and a power budget of 0 dBm offers an improvement of 38% — 63% in minimum rate, while that achieved by a passive RIS is only about 15%, with the same total number of elements.

Intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV) communications are expected to alleviate the load of ground base stations in a cost-effective way. Existing studies mainly focus on the deployment and resource allocation of a single IRS instead of multiple IRSs, whereas it is extremely challenging for joint multi-IRS multi-user association in UAV communications with constrained reflecting resources and dynamic scenarios. To address the aforementioned challenges, we propose a new optimization algorithm for joint IRS-user association, trajectory optimization of UAVs, successive interference cancellation (SIC) decoding order scheduling and power allocation to maximize system energy efficiency. We first propose an inverse soft-Q learning-based algorithm to optimize multi-IRS multi-user association. Then, successive convex approximation (SCA) and Dinkelbach-based algorithm are leveraged to optimize UAV trajectory followed by the optimization of SIC decoding order scheduling and power allocation. Finally, theoretical analysis and performance results show significant advantages of the designed algorithm in convergence rate and energy efficiency.

Multiple input multiple output (MIMO) beamforming has been recognized as a key element to support challenging requirements in the vehicular communication environment. In order to provide accurate beam alignment and tracking results in the high mobility scenario, the mobility induced channel prediction mechanism has been proposed in the line-of-sight (LOS) fading environment, while the application to the practical mixed LOS and non-LOS fading environment is still open. In this paper, we propose a novel mobility and channel prediction combined beamforming (MCPCB) scheme to deal with this issue. Specifically, we rely on per-cluster angular based information and non-linear tracking scheme for angular-delay profile to obtain reliable single input multiple output (SIMO) channel prediction. By linking the estimated mobility parameters and the per-cluster beam directions, our proposed MCPCB scheme is able to provide a higher receiving energy with reduced prediction errors, and achieve more robust prediction performance when the cluster level channel blocking happens. Through analytical and numerical results, we show that the proposed MCPCB scheme can achieve about −50.6 dB and −87.7 dB of average received power gain in the LOS and NLOS scenarios, respectively, and outperform many conventional channel prediction methods.

In this paper, a three-dimensional (3D) geometry-based stochastic model (GBSM) empowered by reconfigurable intelligent surface (RIS) is presented for multiple-input multiple-output (MIMO) vehicle-to-vehicle (V2V) communication systems. Owing to the channel non-stationarity, spherical wavefront, and antenna configurations in RIS-empowered V2V channel, the geometry-based channel models suffer from high computational complexity, thereby leading to high hardware burden. To address this issue, a novel beam domain channel model (BDCM) is generated from the proposed geometry-based channel model through a beamforming operation based on discrete Fourier transform (DFT). To describe the non-stationarities of the V2V channels empowered by RIS, the channel model presented in this paper introduces real-time velocities and accelerations to capture the motion features of the communication terminals. The propagation characteristics including spatial cross-correlation functions (CCFs), temporal autocorrelation functions (ACFs), frequency correlation functions (FCFs), and channel capacities of the proposed communication system are derived and discussed. Some comparisons between the propagation characteristics of the proposed GBSM and those based on BDCM with respect to the different physical parameters of RIS and different environmental variables are investigated. Furthermore, numerical results indicate that the proposed channel model works well by changing the velocity parameters in different motion states.

In this paper, we investigate the realization of covert communication in a general radar-communication cooperation system, which includes integrated sensing and communications as a special example. We explore the possibility of utilizing the sensing ability of radar to track and jam the aerial adversary target attempting to detect the transmission. Based on the echoes from the target, the extended Kalman filtering technique is employed to predict its trajectory as well as the corresponding channels. Depending on the maneuvering altitude of adversary target, two channel state information (CSI) models are considered, with the aim of maximizing the covert transmission rate by jointly designing the radar waveform and communication transmit beamforming vector based on the constructed channels. For perfect CSI under the free-space propagation model, by decoupling the joint design, we propose an efficient algorithm to guarantee that the target cannot detect the transmission. For imperfect CSI due to the multi-path components, a robust joint transmission scheme is proposed based on the property of the Kullback-Leibler divergence. The convergence behaviour, tracking MSE, false alarm and missed detection probabilities, and covert transmission rate are evaluated. Simulation results show that the proposed algorithms achieve accurate tracking. For both channel models, the proposed sensing-assisted covert transmission design is able to guarantee the covertness, and significantly outperforms the conventional schemes.

In this paper, we consider an unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) system, where multiple ground devices offload portions of their latency-sensitive and mission-critical computational tasks to a UAV-carried MEC server for remote computing and compute the remaining portions locally. To meet the low-latency requirements of the MEC, ultra-reliable and low-latency communication (URLLC) is used to offload tasks from the devices to the UAV. We minimize the maximum computation latency among all devices by jointly optimizing the computing times and CPU frequencies of the devices and the UAV, the offloading bandwidths of the devices, and the three-dimensional location of the UAV. We propose an algorithm that decomposes the joint optimization problem into three subproblems, which optimize the UAV’s horizontal location, the UAV’s altitude, and the offloading bandwidths and computing CPU frequencies, respectively. In solving the subproblems, the data rate expression of the devices’ finite-blocklength offloading is accurately approximated by a tractable logarithmic function, and the successive convex approximation technique is applied to tackle the non-convex structure. Furthermore, a semi-closed-form solution to the subproblem that optimizes the bandwidths and CPU frequencies is derived to reduce the complexity. Simulation results show that the proposed algorithm can significantly reduce the system’s computation latency compared to the benchmark schemes.

In this paper, we develop a hybrid multiple access (MA) protocol for an intelligent reflecting surface (IRS) aided uplink transmission network by incorporating the IRS-aided time-division MA (I-TDMA) protocol and the IRS-aided non-orthogonal MA (I-NOMA) protocol as special cases. Two typical communication scenarios, namely the transmit power limited case and the transmit energy limited case are considered, where the device’s rearranged order, time and power allocation, as well as dynamic IRS beamforming patterns over time are jointly optimized to minimize the sum transmission delay. To shed light on the superiority of the proposed IRS-aided hybrid MA (I-HMA) protocol over conventional protocols, the conditions under which I-HMA outperforms I-TDMA and I-NOMA are revealed by characterizing their corresponding optimal solution. Then, a computationally efficient algorithm is proposed to obtain the high-quality solution to the corresponding optimization problems. Simulation results validate our theoretical findings, demonstrate the superiority of the proposed design, and draw some useful insights. Specifically, it is found that the proposed protocol can significantly reduce the sum transmission delay by combining the additional gain of dynamic IRS beamforming with the high spectral efficiency of NOMA, which thus reveals that integrating IRS into the proposed HMA protocol is an effective solution for delay-aware optimization. Furthermore, it reveals that the proposed design reduces the time consumption not only from the system-centric view, but also from the device-centric view.

In an intelligent reflecting surface (IRS)-assisted wireless communication system, obtaining the real-time channel state information (CSI) through channel estimation (CE) is crucial for achieving the IRS’s passive beamforming gain, which however shortens the effective data transmission time due to the CSI feedback overhead. It is of utmost importance to decide how often to estimate the channels in an IRS-assisted system. In this paper, we propose an integrated CE and beamforming scheme to jointly optimize the adaptive CE interval and passive beamforming strategy, based on the past observation sequences composed of imperfect CSI and data rate feedback. We formulate the two-stage optimization problem as a bilevel partially observable Markov decision process (POMDP), aiming to maximize the expectation of cumulative throughput of the system. We propose two bilevel deep reinforcement learning (DRL) algorithms, namely recurrent neural network (RNN) based proximal policy optimization (PPO) algorithm and Belief-based PPO algorithm, to solve this problem. In these two algorithms, the CSI features from the past observation sequences are implicitly extracted by the RNN network or explicitly inferred by the belief network, which then serve as the inputs for the two-stage policy networks to determine the necessity of CE and the IRS beamforming vector based on the PPO algorithm. Simulation results demonstrate the superiority of the proposed adaptive CE scheme over the periodic counterpart in terms of throughput. Moreover, the results show that it is profitable to estimate the channels less frequently if the channels exhibit a higher correlation across time.

Full frequency reuse combined with precoding is a promising solution for multibeam satellite systems (MSSs) to meet the evergrowing capacity demand. However, line-of-sight-dominant satellite-ground channels will cause severe channel correlation among the geographically clustered hotspot users (HUs), which restricts multiuser capacity over HUs. In this paper, we propose the reconfigurable intelligent surface (RIS)-aided hotspot capacity enhancement scheme for MSSs. We formulate a hotspot sum rate maximization problem with SINR constraints added on a different user set and present an alternating optimization (AO)-based algorithm for its solution. To reduce computational complexity, we propose a two-stage algorithm that sequentially optimizes RIS phase shift with manifold optimization and satellite precoding, no longer resorting to AO. The RIS phase shift design utilizes semi-orthogonal subspace maximization and pairwise channel decorrelation. This design effectively formulates the interplay between RIS phase shifts and transmit beamforming related to the SINR constraint. To circumvent high channel estimation overhead, we extend the algorithms to low-cost designs exploiting statistical channel state information. Simulation results demonstrate that our proposed RIS-aided MSS designs substantially enhance HUs’ sum rate, attributed to the RIS-enabled channel refinement mechanism. Moreover, the two-stage algorithm achieves a comparable performance to the AO-based algorithm.

Unmanned aerial vehicles (UAVs) have been demonstrated to be a prominent component for wireless communications. In this work, we consider an emergency communication scenario wherein a UAV-based relay system collects data from ground users, and then uses different UAV-enabled virtual antenna arrays (UVAAs) to transmit the collected data to several remote base stations (BSs) via collaborative beamforming (CB). However, several adjacent aerial users (AUs) are carrying out other missions at the same time, which may be interfered by the signal transmitted by the UVAAs. Thus, we formulate a reliable and energy-efficient communication multi-objective optimization problem (RECMOP) to jointly maximize the minimum receiving signal-to-noise ratio (SNR) of the BSs, minimize the maximum average receiving SNR of the AUs, and minimize the propulsion power consumption of the UAVs, so that diminishing the energy cost while enhancing the system performance. The formulated RECMOP is intricate since it is proven to be NP-hard and non-convex. Therefore, an improved multi-objective gravitational search algorithm (IMOGSA) with several specific designs is proposed to handle the formulated problem. Simulation results manifest that the proposed IMOGSA can effectively solve the formulated RECMOP, and it outperforms other benchmarks in both smaller and larger scale UAV networks. Moreover, extended simulation demonstrates the robustness of the proposed CB-based approach under several unexpected circumstances.

Reconfigurable intelligent surface (RIS) has been envisioned as a highly promising technology for future wireless communication networks. Very recently, a novel beyond diagonal (BD)-RIS architecture has been proposed. This new architecture remarkably extends the traditional diagonal RIS model and yields much more powerful beamforming capability. Meanwhile, however, the emerging symmetry and orthogonality conditions imposed onto BD-RIS’ reflection matrix make its optimization highly difficult, especially when BD-RIS must satisfy numerous additional constraints. This difficulty arises in many BD-RIS applications and has remained unsolved so far. To resolve the above challenge, leveraging the penalty dual decomposition methodology, this paper proposes a novel unified approach that can optimize BD-RIS configuration when it is involved in any number of nonconvex constraints. Especially, we utilize our new approach to solve the power minimization and energy efficiency maximization problems when BD-RIS involves multiple quality-of-service constraints, which have not yet been solved in the literature. Besides, our new approach can also efficiently solve the sum-rate maximization in the BD-RIS assisted system by providing a new analytic-update-based solution, which is more efficient than existing methods. Extensive numerical results demonstrate the effectiveness of our new approach and the significant benefit of BD-RIS over the conventional diagonal RIS.

In this paper, we investigate a practical structure of reconfigurable intelligent surface (RIS)-based double spatial scattering modulation (DSSM) for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. A suboptimal detector is proposed, in which the beam direction is first demodulated according to the received beam strength, and then the remaining information is demodulated by adopting the maximum likelihood algorithm. Based on the proposed suboptimal detector, we derive the conditional pairwise error probability expression. Further, the exact numerical integral and closed-form expressions of unconditional pairwise error probability (UPEP) are derived via two different approaches. To provide more insights, we derive the upper bound and asymptotic expressions of UPEP. In addition, the diversity gain of the RIS-DSSM scheme was also given. Furthermore, the union upper bound of average bit error probability (ABEP) is obtained by combining the UPEP and the number of error bits. Simulation results are provided to validate the derived upper bound and asymptotic expressions of ABEP. We found an interesting phenomenon that the ABEP performance of the proposed system-based phase shift keying is better than that of the quadrature amplitude modulation. Additionally, the performance advantage of ABEP is more significant with the increase in the number of RIS elements.

Reconfigurable intelligent surface (RIS) is a promising technology for future wireless communication systems. Channel estimation (CE) of RIS device is a critical but also challenging issue for its development. The mainstream of existing CE methods is confined to the so-called cascaded channel (CscdChn) estimation scheme, which treats the multiplicative two-hop RIS channels as an effective one and measures it as a whole. This CscdChn training method suffers from severe double-fading attenuation loss, which significantly degrades the CE accuracy. In this paper, we propose a novel RIS-transmitting (RIS-TX) based CE scheme, which has lower pilot overhead than CscdChn scheme and effectively overcomes the double-fading curse via incorporating only one single transmit radio frequency (RF)-chain into RIS. We develop highly efficient gradient descent (GD) and penalty duality decomposition (PDD)-based solutions to resolve the pilot design task for the RIS-TX CE scheme, which is a difficult quartic optimization problem. Our designed pilot signal outperforms the discrete Fourier transform (DFT) sequence, which is reported to be optimal for CscdChn scheme. Besides, both theoretical analysis and numerical results demonstrate that our proposed RIS-TX scheme exhibits distinct performance characteristics as opposed to its CscdChn counterpart and yields superior accuracy when RIS device is not extremely large.

In this study, we explore the performance of a reconfigurable reflecting surface (RIS)-assisted transmit spatial modulation (SM) system for downlink transmission, wherein the deployment of RIS serves the purpose of blind area coverage within the channel. At the receiving end, we present three detectors, i.e., maximum likelihood (ML) detector, two-stage ML detection, and greedy detector to recover the transmitted signal. By utilizing the ML detector, we initially derive the conditional pair error probability expression for the proposed scheme. Subsequently, we leverage the central limit theorem (CLT) to obtain the probability density function of the combined channel. Following this, the Gaussian-Chebyshev quadrature method is applied to derive a closed-form expression for the unconditional pair error probability and establish the union tight upper bound for the average bit error probability (ABEP). Furthermore, we derive a closed-form expression for the ergodic capacity of the proposed RIS-SM scheme. Monte Carlo simulations are conducted not only to assess the complexity and reliability of the three detection algorithms but also to validate the results obtained through theoretical derivation results.

In this paper, we focus on simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted multiuser multiple-input single-output (MISO) interference channels (ICs), where the RIS can serve the users in both forward and backward half-spaces at the same time by transmitting and reflecting incident signals. To suppress the inevitable inter-user interference, we adopt improper Gaussian signaling (IGS) and propose to jointly optimize the beamforming vectors of APs and the passive transmission and reflection coefficients of RIS to maximize the minimum achievable information rate among these users. In order to address the non-convex optimization problem, we provide an efficient iterative optimization algorithm to attain high-quality solutions. Numerical results demonstrate the superiority of IGS over conventional proper Gaussian signaling in STAR-RIS assisted MU-MISO ICs.

This paper investigates an intelligent reflecting surface (IRS) to enhance the physical layer security of an integrated sensing and communication (ISAC) system in which the IRS is deployed to not only assist the downlink communication for multiple users, but also create a virtual line-of-sight (LoS) link for target sensing. In particular, we consider a challenging scenario where the target may be a suspicious eavesdropper that potentially intercepts the communication-user information transmitted by the base station (BS). To ensure the sensing quality while preventing the eavesdropping, dedicated sensing signals are transmitted by the BS. We investigate the joint design of the phase shifts at the IRS and the communication as well as radar beamformers at the BS to maximize the sensing beampattern gain towards the target, subject to the maximum information leakage to the eavesdropping target and the minimum signal-to-interference-plus-noise ratio (SINR) required by users. To solve this non-convex optimization problem, a penalty-based algorithm is proposed to obtain a high-quality solution. In particular, the beamformers are obtained with a semi-closed-form solution using Lagrange duality and the IRS phase shifts are solved for in closed form by applying the majorization-minimization (MM) method. Simulation results show that dedicated sensing signals are required to further improve the system performance, and also validate the tremendous potential of IRS to achieve significant beampattern gains and guarantee ISAC system security.

In this paper, we investigate a state-of-the-art reconfigurable intelligent surface (RIS)-assisted spatial scattering modulation (SSM) scheme for millimeter-wave (mmWave) systems, where a more practical scenario that the RIS is near the transmitter while the receiver is far from RIS is considered. To this end, the line-of-sight (LoS) and non-LoS links are utilized in the transmitter-RIS and RIS-receiver channels, respectively. By employing the maximum likelihood detector at the receiver, the conditional pairwise error probability (CPEP) expression for the RIS-SSM scheme is derived under the two scenarios that the received beam demodulation is correct or not. Furthermore, the union upper bound of average bit error probability (ABEP) is obtained based on the CPEP expression. Finally, the derivation results are exhaustively validated by the Monte Carlo simulations.

This paper studies integrated sensing and communication (ISAC) technology in a full-duplex (FD) uplink communication system. As opposed to the half-duplex system, where sensing is conducted in a first-emit-then-listen manner, FD ISAC system emits and listens simultaneously and hence conducts uninterrupted target sensing. Besides, impressed by the recently emerging reconfigurable intelligent surface (RIS) technology, we also employ RIS to improve the self-interference (SI) suppression and signal processing gain. As will be seen, the joint beamforming, RIS configuration and mobile users' power allocation is a difficult optimization problem. To resolve this challenge, via leveraging the cutting-the-edge majorization-minimization (MM) and penalty-dual-decomposition (PDD) methods, we develop an iterative solution that optimizes all variables via using convex optimization techniques. Numerical results demonstrate the effectiveness of our proposed solution and the great benefit of employing RIS in the FD ISAC system.

Vehicle-to-everything (V2X) communication is expected to support many promising applications in next-generation wireless networks. The recent development of integrated sensing and communications (ISAC) technology offers new opportunities to meet the stringent sensing and communication (S&C) requirements in V2X networks. However, considering the relatively small radar cross section (RCS) of the vehicles and the limited transmit power of the road site units (RSUs), the power of echoes may be too weak to achieve effective target detection and tracking. To handle this issue, we propose a novel sensing-assisted communication scheme by employing an intelligent omni-surface (IOS) on the surface of the vehicle. First, a two-phase ISAC protocol, including the S&C phase and the communication-only phase, was presented to maximize the throughput by jointly optimizing the IOS phase shifts and the sensing duration. Then, we derive a closed-form expression of the achievable rate which achieves a good approximation. Furthermore, a sufficient and necessary condition for the existence of the S&C phase is derived to provide useful insights for practical system design. Simulation results demonstrate the effectiveness of the proposed sensing-assisted communication scheme in achieving a high throughput with low transmit power requirements.

Despite its high energy and hardware efficiency, some defects of the reconfigurable intelligence surface (RIS) technology have come to be realized, including the severe fading loss and restricted-to-half-space coverage. This paper proposes a novel double-faced-active (DFA)-RIS structure to overcome these defects. Besides, we utilize this novel DFA-RIS to improve power saving of the communication system. Unlike traditional power saving literature, we aim at fulfilling queueing stability and long-term power minimization in a downlink system assisted by the DFA-RIS, with a realistic data arriving process taken into consideration. Enlightened by Lyapunov control theory, we propose an online optimization strategy that adaptively adjusts DFA-RIS configuration. Each online problem can be efficiently solved by leveraging alternative directional method of multipliers (ADMM) method. Numerical results demonstrate the effectiveness of our proposed Lyapunov-guided strategy and DFA-RIS’ superiority over the classical passive RIS.

The rising reconfigurable intelligent surface (RIS) is a promising technology and a multitude of literature focuses on its channel estimation (CE), which is a critical and challenging task. Most existing works adopt a type of “cascaded channel” training scheme, where the “two-hop” channel cascaded by RIS is treated as one and measured by one shot. As unveiled by the latest researches, however, the concatenated channel suffers from severe fading loss and hence seriously degrades the CE precision. To resolve this difficulty, this paper proposes a novel training scheme. Specifically, being equipped with one transmit RF chain, the RIS broadcasts pilot signals to all other devices during the training period. This novel scheme can overcome double fading loss at a low hardware cost. The pilot design of the newly proposed training scheme is a difficult quartic optimization problem and we develop a gradient descent (GD) based solution to resolve it. Numerical results verify the effectiveness of our solution and demonstrate our training scheme can significantly outperform the traditional cascaded channel training method.

We investigate a new self-sustainable intelligent omni-surface (S-IOS) aided multi-user wireless network, where the S-IOS harvests the radio frequency energy from the signals transmitted by the access point (AP) and exploits the harvested energy to provide full-dimensional beamforming services for the users. We design a joint optimization problem of transmit beamforming at the AP, refraction/reflection beamforming at the S-IOS, and energy harvesting schedule at the S-IOS, to maximize the sum rate of the overall network. A computationally-efficient algorithm is developed to solve the problem. Numerical results demonstrate the significant importance of the S-IOS for spectral and energy efficient wireless communications.

Reconfigurable intelligent surface (RIS), as an emerging technology, has recently been applied to expand the range of mobile-edge computing (MEC) networks and improve wireless environments. However, current terrestrial RIS-assisted MEC networks have some limitations, such as severe signal attenuation and inflexible equipment deployment. To take full advantage of the superiority of the RIS, this article considers an aerial RIS (ARIS)-assisted aerial computing scheme, where the ARIS and the other unmanned aerial vehicle (UAV) equipped with a MEC server are employed to facilitate offloading computing tasks from Internet of Things (IoT) user equipments (UEs) to the access point (AP). With the flexibility of the dual-UAV, we can mitigate the Non-Line-of-Sight (NLoS) air–ground paths caused by obstacles. In the proposed scenario, to improve the system energy efficiency while ensuring the UEs receive high-quality wireless services, we intend to jointly optimize the trajectories of the two UAVs, the phase shift of the ARIS, the computation offloading strategy, and computation resource allocation. The issue is formulated as a mixed nonconvex optimization problem, so it is difficult to solve it in time for adapting different environments by using conventional convex optimization methods. However, we develop a double deep$Q$-network (DDQN)-based algorithm to obtain the near-optimal online decision-making solution. Simulation findings indicate that the proposed DDQN-based algorithm can effectively increase the energy efficiency of the proposed dual-UAV cooperative MEC system in comparison to the benchmark schemes.

In this article, we propose a state-of-the-art downlink communication transceiver design for transmissive reconfigurable metasurface (RMS)-enabled simultaneous wireless information and power transfer (SWIPT) networks. Specifically, a feed antenna is deployed in the transmissive RMS-based transceiver, which can be used to implement beamforming. According to the relationship between wavelength and propagation distance, the spatial propagation models of plane and spherical waves are built. Then, in the case of imperfect channel state information (CSI), we formulate a robust system sum-rate maximization problem that jointly optimizes RMS transmissive coefficient, transmit power allocation, and power splitting ratio design while taking account of the nonlinear energy harvesting model and outage probability criterion. Since the coupling of optimization variables, the whole optimization problem is nonconvex and cannot be solved directly. Therefore, the alternating optimization (AO) framework is implemented to decompose the nonconvex original problem. In detail, the whole problem is divided into three subproblems to solve. For the nonconvexity of the objective function, successive convex approximation (SCA) is used to transform it, and the penalty function method and difference-of-convex (DC) programming are applied to deal with the nonconvex constraints. Finally, we alternately solve the three subproblems until the entire optimization problem converges. Numerical results show that our proposed algorithm has convergence and better performance than other benchmark algorithms.

In wireless sensor networks, unmanned aerial vehicles (UAVs) can be employed to collect data from sensor nodes (SNs) efficiently. In this article, we consider a dual-UAV-enabled (long-distance) data collection system, where one UAV is dispatched to collect data from distributed SNs, while the other UAV is employed to relay data from the data-collection UAV to a fusion center (FC) that locates far from the SNs. To shorten the time duration for the FC to collect all data, we propose to minimize the completion time of the data collection task by jointly optimizing the transmit power and bandwidth of all SNs and the UAVs, as well as the three-dimensional trajectories of the two UAVs. Instead of assuming the simplified line-of-sight UAV-ground channel model as in most existing works, we model the channels between the UAVs and SNs as well as that between the UAVs and FC by applying the practically more accurate elevation-angle-dependent Rician fading channel model. The resulting optimization problem is nonconvex and thus is difficult to solve in general. Nevertheless, we propose an algorithm to solve it efficiently by using the techniques of block coordinate descent, slack variable substitution, and successive convex approximation. Simulation results show that our proposed algorithm can achieve higher communication efficiency than other benchmark schemes and greatly reduce the task completion time for data collection.

To facilitate the development of Internet of Things (IoT) services, future networks are expected to simultaneously provide sensing functionality and support low-power communications. In this article, we investigate the system sum-rate maximization problem in an integrated sensing and reconfigurable intelligent surface (RIS) backscatter communication system, where the base station (BS) simultaneously detects backscattered signals from multiple IoT devices and senses targets based on the echo signals. We formulate a joint transmit beamforming, RIS phase shifts, and receive beamforming design problem under the Cramér–Rao bound (CRB) constraint for target angle estimation. To solve the nonconvex problem, we then propose a fractional programming (FP)-based alternating optimization algorithm. In particular, the FP technique is first employed to transform the formulated problem into a more tractable form, and the exact penalty method and manifold optimization are then utilized to address the CRB constraint and constant-modulus constraint, respectively. Numerical results have shown that the proposed design significantly improves the system sum rate and illustrates the tradeoff between the communication and sensing performance.

In this paper, we develop a unified dynamic intelligent reflecting surface (IRS) beamforming framework to boost the sum computation rate of an IRS-aided mobile edge computing (MEC) system, where each device follows a binary offloading policy. Specifically, the task of each device has to be either executed locally or offloaded to MEC servers as a whole with the aid of given number of IRS beamforming vectors available. By flexibly controlling the number of times for IRS reconfiguring phase-shifts, the system can achieve a balance between the performance and associated signalling overhead. We aim to maximize the sum computation rate by jointly optimizing the computational mode selection for each device, offloading time allocation, and IRS beamforming vectors across time. Since the resulting optimization problem is non-convex and NP-hard, there are generally no standard methods to solve it optimally. To tackle this problem, we first propose a penalty-based successive convex approximation algorithm, where all the associated variables in the inner-layer iterations are optimized simultaneously and the obtained solution is guaranteed to be locally optimal. Then, we further derive the offloading activation condition for each device by deeply exploiting the intrinsic structure of the original optimization problem. According to the offloading activation condition, a low-complexity algorithm based on the successive refinement method is proposed to obtain high-quality suboptimal solutions, which are more appealing for practical systems with a large number of devices and IRS elements. Moreover, the optimal condition for the proposed low-complexity algorithm is revealed. The effectiveness of the proposed algorithms is demonstrated through numerical examples. In addition, the results illustrate the practical significance of the IRS in MEC systems for achieving coverage extension and supporting multiple energy-limited devices for task offloading, and also unveil the fundamental performance-cost tradeoff embedded in the proposed dynamic IRS beamforming framework.

Intelligent reflecting surface (IRS) has recently received significant attention in wireless networks owing to its ability to smartly control the wireless propagation through passive reflection. Although prior works have employed the IRS to enhance the system performance under various setups, the fundamental capacity limits of an IRS aided multi-antenna multi-user system have not yet been characterized. Motivated by this, we investigate an IRS aided multiple-input single-output (MISO) broadcast channel by considering the capacity-achieving dirty paper coding (DPC) scheme and dynamic beamforming configurations. We first propose a bisection based framework to characterize its capacity region by optimally solving the sum-rate maximization problem under a set of rate constraints, which is also applicable to characterize the achievable rate region with the zero-forcing (ZF) scheme. Interestingly, it is rigorously proved that dynamic beamforming is able to enlarge the achievable rate region of ZF if the IRS phase-shifts cannot achieve fully orthogonal channels, whereas the attained gains become marginal due to the reduction of the channel correlations induced by smartly adjusting the IRS phase-shifts. The result implies that employing the IRS is able to reduce the demand for implementing dynamic beamforming. Finally, we analytically prove that the sum-rate achieved by the IRS aided ZF is capable of approaching that of the IRS aided DPC with a sufficiently large IRS in practice. Simulation results shed light on the impact of the IRS on transceiver designs and validate our theoretical findings, which provide useful guidelines to practical systems by indicating that replacing sophisticated schemes with easy-implementation schemes would only result in slight performance loss.

Reconfigurable intelligent surface (RIS) is a promising technique that smartly reshapes wireless propagation environment in the future wireless networks. In this paper, we apply RIS to an unmanned aerial vehicle (UAV)-assisted non-orthogonal multiple access (NOMA) network, in which the transmit signals from multiple UAVs to ground users are strengthened through RIS. Our objective is to minimize the power consumption of the system while meeting the constraints of minimum data rate for users and minimum inter-UAV distance. The formulated optimization problem is non-convex by jointly optimizing the position of UAVs, RIS reflection coefficients, transmit power, active beamforming vectors and decoding order, and thus is quite hard to solve optimally. To tackle this problem, we divide the resultant optimization problem into four independent subproblems, and solve them in an iterative manner. In particular, we first consider the sub-solution of UAVs placement which can be obtained via the successive convex approximation (SCA) and maximum ratio transmission (MRT). By applying the Gaussian randomization procedure, we yield the closed-form expression for the RIS reflection coefficients. Subsequently, the transmit power is optimized using standard convex optimization methods. Finally, a dynamic-order decoding scheme is presented for optimizing the NOMA decoding order in order to guarantee fairness among users. Simulation results verify that our designed joint UAV deployment and resource allocation scheme can effectively reduce the total power consumption compared to the benchmark methods, thus verifying the advantages of combining RIS into the multi-UAV assisted NOMA networks.

Although the reconfigurable intelligent surface (RIS) technology is envisioned promising to enhance communication from all aspects, including physical-layer security, increasing concerns have lately been cast onto its defects—the severe “double-fading” loss and its confined-to-half-space coverage. Diverse novel RIS architectures have recently emerged to partially overcome these shortcomings, yet perfect solution is still absent. This paper proposes a novel double-faced active (DFA)-RIS structure to surmount the above two prominent defects simultaneously. Furthermore, we utilize the DFA-RIS to promote secrecy performance via jointly designing access point (AP)’s beamforming and DFA-RIS configuration towards maximizing sum secrecy rate (SR). The optimization problem is highly challenging due to the constraints deriving from the DFA-RIS architecture, especially the presence of power splitting parameters. By leveraging majorization–minimization (MM) and penalty dual decomposition (PDD) methods, we develop an efficient solution that updates all variables via convex optimization techniques. Our proposed solution is significant and general as it is applicable to all other cutting-the-edge RIS architectures to maximize sum SR, which has not yet been thoroughly worked out. Numerical results verify the convergence and effectiveness of our proposed algorithm and demonstrate that our proposed DFA-RIS architecture outperforms all other state-of-the-art RIS techniques to enhance communication security.

In this paper, we propose a new simultaneous information relaying and jamming (SIRJ) scheme based on a dual-functional reconfigurable intelligent surface (RIS) to achieve secure non-orthogonal multiple access communications. Specifically, the RIS elements are split into two groups, where elements in one group perform signal reflection to enhance the legitimate reception quality while elements in the other group generate artificial jamming to interfere with the eavesdropper (Eve). Based on different channel state information (CSI) availabilities of Eve, the system sum-rate is maximized by jointly optimizing the transmit beamforming of the base station, reflect beamforming of the RIS, and mode selection of each RIS element, subject to a maximum tolerable information leakage to Eve. For the case with perfect Eve’s CSI, a penalty based alternating algorithm is proposed to deal with the challenging multivariate coupled and mixed integer non-convex optimization problem. For the case with imperfect Eve’s CSI, we consider the infinite number of secrecy constraints, for which the traditional$\mathcal {S}$-procedure cannot be directly applied. To tackle this challenge, we devise an efficient transformation that fits the$\mathcal {S}$-procedure to the problem and propose a robust secure beamforming design. Simulation results demonstrate the performance advantage of the proposed SIRJ scheme over the existing baseline schemes.

Reconfigurable intelligent surface (RIS) has been deemed as one of potential components of future wireless communication systems because it can adaptively manipulate the wireless propagation environment with low-cost passive devices. However, due to the severe double path loss, the traditional passive RIS can provide sufficient gain only when receivers are very close to the RIS. Moreover, RIS cannot provide signal coverage for the receivers at the back side of it. To address these drawbacks in practical implementation, we introduce a novel reflection and transmission dual-functional active RIS (DF-ARIS) architecture in this paper, which can simultaneously realize reflection and transmission functionalities with active signal amplification to significantly extend signal coverage and enhance the quality-of-service (QoS) of all users. The problem of joint transmit beamforming and dual-functional active RIS design is investigated in RIS-enhanced multiuser multiple-input single-output (MU-MISO) systems. Both sum-rate maximization and power minimization problems are considered. To address their non-convexity, we develop efficient iterative algorithms to decompose them into several separate design problems, which are efficiently solved by exploiting fractional programming (FP) and Riemannian-manifold optimization techniques. Simulation results demonstrate the superiority of the proposed dual-functional active RIS architecture and the effectiveness of our proposed algorithms over various benchmark schemes.

Reconfigurable intelligent surface (RIS) is able to manipulate the wireless environment smartly and has been exploited for assisting the wireless communications, especially at high frequency band. However, it suffers from hardware impairments (HWIs) in practical design, manufacturing and deployment, which inevitably degrades its performance and thus limits its full potential. To address this practical issue, we first propose a new RIS reflection model involving phase-shift errors, which is verified by the measurement results from field trials. With this beamforming model, various phase-shift errors caused by different HWIs can be analyzed. The phase-shift errors are classified into three categories: 1) globally independent and identically distributed errors; 2) grouped independent and identically distributed errors; and 3) grouped fixed errors. The impact of typical HWIs, including frequency mismatch, PIN diode failures and panel deformation, on RIS beamforming ability are studied with the theoretical model and are verified with numerical and field test data. The impact of frequency mismatch are discussed separately for narrow-band and wide-band beamforming. Finally, useful insights and guidelines on the RIS design and its deployment are highlighted for practical wireless sytsems.

-1To efficiently exploit the massive amounts of raw data that are increasingly being generated in mobile edge networks, federated learning (FL) has emerged as a promising distributed learning technique by collaboratively training a shared learning model on edge devices. The number of resource blocks when using traditional orthogonal transmission strategies for FL linearly scales with the number of participating devices, which conflicts with the scarcity of communication resources. To tackle this issue, over-the-air computation (AirComp) has emerged recently which leverages the inherent superposition property of wireless channels to performone-shotmodel aggregation. However, the aggregation accuracy in AirComp suffers from the unfavorable wireless propagation environment. In this paper, we consider the use of intelligent reflecting surfaces (IRSs) to mitigate this problem and improve FL performance with AirComp. Specifically, a novel performance-oriented long-term design scheme that integrated design multiple communication rounds to minimize the optimality gap of the loss function is proposed. We first analyze the convergence behavior of the FL procedure with the absence of channel fading and noise. Based on the obtained optimality gap which characterizes the impact of channel fading and noise in different communication rounds on the ultimate performance of FL, we propose both online and offline schemes to tackle the resulting design problem. Simulation results demonstrate that such a long-term design strategy can achieve higher test accuracy than the conventional isolated design approach in FL. Both the theoretical analysis and numerical results exhibit a “later-is-better” principle, which demonstrates the later rounds in the FL procedure are more sensitive to aggregation error, and hence more resources are required over time.

Although reconfigurable intelligent surface (RIS) technology has manifested great potentials in improving wireless network’s power saving, most existing literature restricts to pure physical (PHY) layer beamforming design and neglects the impact of media access control (MAC) layer’s data traffic flows. Simultaneously, current RIS technology suffers from defects — the severe fading loss and the limitation of half-space coverage. This paper aims to perform a cross-layer design via jointly optimizing MAC layer scheduling and PHY layer RIS beamforming to reduce power consumption. Besides, we propose a novel double-faced-active (DFA)-RIS architecture to promote RIS’ capability. The proposed design task leads to a highly challenging stochastic problem to minimize long-term power consumption while stabilizing queues. Inspired by Lyapunov control theory, we propose an online optimization strategy to resolve this challenge. Via exploiting alternative directional method of multipliers (ADMM), we develop an analytic-based solution to solve the online sub-problems highly efficiently without resorting to any numerical solvers. Our strategy theoretically guarantees all queues’ stability and achieves a tunable trade-off between the power expenditure and queue lengths. Extensive numerical results are presented to demonstrate the effectiveness of our proposed cross-layer design and the DFA-RIS’ advantage over other cutting-the-edge RIS architectures.

This paper investigates the reconfigurable intelligent surface (RIS) assisted spatial scattering modulation (SSM) scheme for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems, in which line-of-sight (LoS) and non-line-of-sight (NLoS) paths are respectively considered in the transmitter-RIS and RIS-receiver channels. Based on the maximum likelihood detector, the expression for the conditional pairwise error probability (CPEP) of the RIS-SSM scheme is derived for both cases of correct demodulation of the received beam or not. Furthermore, we derive the closed-form expressions of the unconditional pairwise error probability (UPEP) by employing two different methods: the probability density function and the moment-generating function expressions with a descending order of scatterer gains. To provide more useful insights, we derive the asymptotic UPEP and the diversity gain of the RIS-SSM scheme in the high SNR region. Depending on UPEP and the corresponding Euclidean distance, we further give the union upper bound of the average bit error probability (ABEP). To acquire the effective capacity of the proposed system, a new framework for ergodic capacity analysis is also provided. Finally, all derivation results are validated via extensive Monte Carlo simulations and reveal that the proposed RIS-SSM scheme outperforms the benchmarks in terms of reliability.

This work presents non-orthogonal multiple access (NOMA) enabled energy-efficient alternating optimization framework for backscatter aided wireless powered uplink sensors communications for beyond 5G intelligent transportation system (ITS). Specifically, the transmit power of carrier emitter (CE) and reflection coefficients of backscatter aided roadside sensors are optimized with channel uncertainties for the maximization of the energy efficiency (EE) of the network. The formulated problem is tackled by the proposed two-stage alternating optimization algorithm named AOBWS (alternating optimization for backscatter aided wireless powered sensors). In the first stage, AOBWS employs an iterative algorithm to obtain optimal CE transmit power through simplified closed-form computed through Cardano’s formulae. In the second stage, AOBWS uses a non-iterative algorithm that provides a closed-form expression for the computation of optimal reflection coefficient for roadside sensors under their quality of service (QoS) and a circuit power constraint. The global optimal exhaustive search (ES) algorithm is used as a benchmark. Simulation results demonstrate that the AOBWS algorithm can achieve near-optimal performance with very low complexity, which makes it suitable for practical implementations.

Intelligent reflecting surface (IRS) has been widely considered as one of the key enabling techniques for future wireless communication networks owing to its ability of dynamically controlling the phase shift of reflected electromagnetic (EM) waves to construct a favorable propagation environment. While IRS only focuses on signal reflection, the recently emerged innovative concept of intelligent omni-surface (IOS) can provide the dual functionality of manipulating reflecting and transmitting signals. Thus, IOS is a new paradigm for achieving ubiquitous wireless communications. In this paper, we consider an IOS-assisted multi-user multi-input single-output (MU-MISO) system where the IOS utilizes its reflective and transmissive properties to enhance the MU-MISO transmission. Both power minimization and sum-rate maximization problems are solved by exploiting the second-order cone programming (SOCP), Riemannian manifold, weighted minimum mean square error (WMMSE), and block coordinate descent (BCD) methods. Simulation results verify the advancements of the IOS for wireless systems and illustrate the significant performance improvement of our proposed joint transmit beamforming, reflecting and transmitting phase-shift, and IOS energy division design algorithms. Compared with conventional IRS, IOS can significantly extend the communication coverage, enhance the strength of received signals, and improve the quality of communication links.

Reconfigurable intelligent surface (RIS) has been developed as a promising approach to enhance the performance of fifth-generation (5G) systems through intelligently reconfiguring the reflection elements. However, RIS-assisted beamforming design highly depends on the channel state information (CSI) and RIS’s location, which could have a significant impact on system performance. In this paper, the robust beamforming design is investigated for a RIS-assisted multiuser millimeter wave system with imperfect CSI, where the weighted sum-rate maximization problem (WSM) is formulated to jointly optimize transmit beamforming of the BS, RIS placement and reflect beamforming of the RIS. The considered WSM maximization problem includes CSI error, phase shifts matrices, transmit beamforming as well as RIS placement variables, which results in a complicated nonconvex problem. To handle this problem, the original problem is divided into a series of subproblems, where the location of RIS, transmit/reflect beamforming and CSI error are optimized iteratively. Then, a multiobjective evolutionary algorithm is introduced to gradient projection-based alternating optimization, which can alleviate the performance loss caused by the effect of imperfect CSI. Simulation results reveal that the proposed scheme can potentially enhance the performance of existing wireless communication, especially considering a desirable trade-off among beamforming gain, user priority and error factor.

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

In this paper, we investigate the fundamental multiple access (MA) scheme in an active intelligent reflecting surface (IRS) aided energy-constrained Internet-of-Things (IoT) system, where an active IRS is deployed to assist the uplink transmission from multiple IoT devices to an access point (AP). Our goal is to maximize the sum throughput by optimizing the IRS beamforming vectors across time and resource allocation. To this end, we first study two typical active IRS aided MA schemes, namely time division multiple access (TDMA) and non-orthogonal multiple access (NOMA), by analytically comparing their achievable sum throughput and proposing corresponding algorithms. Interestingly, we prove that given only one available IRS beamforming vector, the NOMA-based scheme generally achieves a larger throughput than the TDMA-based scheme, whereas the latter can potentially outperform the former if multiple IRS beamforming vectors are available to harness the favorable time selectivity of the IRS. To strike a flexible balance between the system performance and the associated signaling overhead incurred by more IRS beamforming vectors, we then propose a general hybrid TDMA-NOMA scheme with device grouping, where the devices in the same group transmit simultaneously via NOMA while devices in different groups occupy orthogonal time slots. By controlling the number of groups, the hybrid TDMA-NOMA scheme is applicable for any given number of IRS beamforming vectors available. Despite of the non-convexity of the considered optimization problem, we propose an efficient algorithm based on alternating optimization, where each subproblem is solved optimally. Simulation results illustrate the practical superiorities of the active IRS over the passive IRS in terms of the coverage extension and supporting multiple energy-limited devices, and demonstrate the effectiveness of our proposed hybrid MA scheme for flexibly balancing the performance-cost tradeoff.

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

In this paper, we study an intelligent reflecting surface (IRS)-aided radar-communication (Radcom) system, where the IRS is leveraged to help Radcom base station (BS) transmit the joint of communication signals and radar signals for serving communication users and tracking targets simultaneously. The objective of this paper is to minimize the total transmit power at the Radcom BS by jointly optimizing the active beamformers, including communication beamformers and radar beamformers, at the Radcom BS and the phase shifts at the IRS, subject to the minimum signal-to-interference-plus-noise ratio (SINR) required by communication users, the minimum SINR required by the radar, and the cross-correlation pattern design. In particular, we consider two cases, namely, case I and case II, based on the presence or absence of the radar cross-correlation design and the interference introduced by the IRS on the Radcom BS. For case I where the cross-correlation design and the interference are not considered, we prove that the dedicated radar signals are not needed, which significantly reduces implementation complexity and simplifies algorithm design. Then, a penalty-based algorithm is proposed to solve the resulting non-convex optimization problem. Whereas for case II considering the cross-correlation design and the interference, we unveil that the dedicated radar signals are needed in general to enhance the system performance. Since the resulting optimization problem is more challenging to solve as compared with the case I, the semidefinite relaxation (SDR) based alternating optimization (AO) algorithm is proposed. Particularly, instead of relying on the Gaussian randomization technique to obtain an approximate solution by reconstructing rank-one solution, the tightness is achieved by our proposed reconstruction strategy. Simulation results demonstrate the effectiveness of proposed algorithms and also show the superiority of the proposed scheme over various benchmark schemes.

Due to the features of on-demand deployment and flexible observation, unmanned aerial vehicles (UAVs) are promising for serving as the next-generation aerial sensors by using their onboard sensing devices. Compared to a single UAV with limited sensing coverage and communication capability, multi-UAV cooperation is able to realize more effective sensing and transmission (S&T) services, and delivers the sensory data to the control center more efficiently for further analysis. Nevertheless, most existing works on multi-UAV sensing mainly focus on mutually exclusive task allocation and independent data transmission, which did not fully exploit the benefit of multi-UAV sensing and communication. Motivated by this, we propose a novel multi-UAV cooperative S&T scheme with overlapped sensing task allocation. Although overlapped task allocation may sound counter-intuitive, it can actually foster cooperative transmission among multiple UAVs through a virtual multi-antenna system and thus reduce the overall sensing mission completion time. To obtain the optimal task allocation and transmit power of the proposed scheme, a mission completion time minimization problem is formulated. To solve this problem, a condition that specifies whether it is necessary for the UAVs to perform overlapped sensing is derived. For the cases of overlapped sensing, this time minimization problem is transformed into a monotonic optimization and is solved by the generic Polyblock algorithm. To efficiently evaluate the mission completion time in each iteration of the Polyblock algorithm, new auxiliary variables are introduced to decouple the otherwise sophisticated joint optimization of transmission time and power. While for the degenerated case of non-overlapped sensing, the closed-form expression of the optimal transmission time is derived, which provides insights into the optimal solution and facilitates the design of an efficient double-loop binary search algorithm to optimally solve the degenerated problem. Finally, simulation results demonstrate that the proposed scheme significantly reduces the mission completion time over benchmark schemes.

Driven by unmanned aerial vehicle (UAV)’s advantages of flexible observation and enhanced communication capability, it is expected to revolutionize the existing integrated sensing and communication (ISAC) system and promise a more flexible joint design. Nevertheless, the existing works on ISAC mainly focus on exploring the performance of both functionalities simultaneously during the entire considered period, which may ignore the practical asymmetric sensing and communication requirements. In particular, always forcing sensing along with communication may make it is harder to balance between these two functionalities due to shared spectrum resources and limited transmit power. To address this issue, we propose a new integrated periodic sensing and communication (IPSAC) mechanism for the UAV-enabled ISAC system to provide a more flexible trade-off between two integrated functionalities. Specifically, the system achievable rate is maximized via jointly optimizing UAV trajectory, user association, target sensing selection, and transmit beamforming, while meeting the sensing frequency and beam pattern gain requirement for the given targets. Despite that this problem is highly non-convex and involves closely coupled integer variables, we derive the closed-form optimal beamforming vector to dramatically reduce the complexity of beamforming design, and present a tight lower bound of the achievable rate to facilitate UAV trajectory design. Based on the above results, we propose a two-layer penalty-based algorithm to efficiently solve the considered problem. To draw more important insights, the optimal achievable rate and the optimal UAV location are analyzed under a special case of infinity number of antennas. Furthermore, we prove the structural symmetry between the optimal solutions in different ISAC frames without location constraints in our considered UAV-enabled ISAC system. Based on this, we propose an efficient algorithm for solving the problem with location constraints. Numerical results validate the effectiveness of our proposed designs and also unveil a more flexible trade-off in ISAC systems over benchmark schemes.

Reconfigurable intelligent surface (RIS) has been anticipated to be a novel cost-effective technology to improve the performance of future wireless systems. In this paper, we investigate a practical RIS-aided multiple-input-multiple-output (MIMO) system in the presence of transceiver hardware impairments, RIS phase noise and imperfect channel state information (CSI). Joint design of the MIMO transceiver and RIS reflection matrix to minimize the total average mean-square-error (MSE) of all data streams is particularly considered. This joint design problem is non-convex and challenging to solve due to the newly considered practical imperfections. To tackle the issue, we first analyze the total average MSE by incorporating the impacts of the above system imperfections. Then, in order to handle the tightly coupled optimization variables and non-convex NP-hard constraints, an efficient iterative algorithm based on alternating optimization (AO) framework is proposed with guaranteed convergence, where each subproblem admits a closed-form optimal solution by leveraging the majorization-minimization (MM) technique. Moreover, via exploiting the special structure of the unit-modulus constraints, we propose a modified Riemannian gradient ascent (RGA) algorithm for the discrete RIS phase shift optimization. Furthermore, the optimality of the proposed algorithm is validated under line-of-sight (LoS) channel conditions, and the irreducible MSE floor effect induced by imperfections of both hardware and CSI is also revealed in the high signal-to-noise ratio (SNR) regime. Numerical results show the superior MSE performance of our proposed algorithm over the adopted benchmark schemes, and demonstrate that increasing the number of RIS elements is not always beneficial under the above system imperfections.

In this paper, we introduce an intelligent reflecting surface (IRS)-assisted wireless powered heterogeneous network (WPHN) consisting of two heterogeneous groups of devices. Specifically, one group of devices, i.e., energy-harvesting devices (EHDs), are charged by external energy supplies, while the other group of devices, i.e., non-energy-harvesting devices (NEHDs), are powered by internal energy supplies. An IRS aims to participate in the wireless energy transfer (WET) in downlink and the wireless information transfer (WIT) in the uplink. A sum throughput maximization problem is formulated subject to the constraints of individual energy consumption, transmission time scheduling, and IRS phase shifts. To cope with the non-convexity of the problem, we first derive the optimal IRS phase shifts of the uplink WIT independently. Next, the semi-definite programming (SDP) relaxation is adopted to recast this non-convex problem into the convex one, which can be numerically solved. Then, a novel low-complexity scheme is developed to gain more insights and mitigate the computational complexity induced by the SDP relaxation. In particular, the dual problem and Karush-Kuhn-Tucker conditions are first utilized to obtain the optimal transmission time scheduling. Then, we propose a method based on Riemannian manifold optimization to compute the optimal IRS phase shifts of the downlink WET in closed-form. Finally, simulation results are presented to verify the optimality of our proposed scheme, and highlight the benefits induced by the IRS to coordinate these heterogeneous devices.

We consider a novel hybrid active-passive reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV) air-ground communications system. Unlike the conventional passive RIS, the hybrid RIS is equipped with a few active elements to not only reflect but also amplify the incident signals for significant performance improvement. Towards a fairness design, our goal is to maximize the minimum rate among users through jointly optimizing the location and power allocation of the UAV and the RIS reflecting/amplifying coefficients. The formulated optimization problem is nonconvex and challenging, which is efficiently solved via block coordinate descend and successive convex approximation. Our numerical results show that a hybrid RIS requires only 4 active elements and a power budget of 0 dBm to achieve an improvement of 52.08% in the minimum rate, while that achieved by a conventional passive RIS with the same total number of elements is only 18.06%.

Reconfigurable intelligent surface (RIS) is envisioned as a promising technology to enhance future wireless communication systems. Very recently, a novel active RIS architecture has been proposed via introducing amplifiers into the reflecting elements. Although these embedded amplifiers can effectively extend the RIS coverage, they also bring non-negligible energy expenditure. To overcome this drawback, this paper proposes a novel sub-array based structure, which divides the entire RIS array into multiple sub-arrays with each being flexibly turned on/off. We aim to minimize the power consumption of the whole system via jointly activating sub-arrays and designing beamforming, which is highly challenging due to its combinatorial nature. Via inducing the group sparsity and leveraging the majorization-minimization (MM) approach, we develop an efficient solution to resolve this challenge. Numerical results demonstrate that our proposed sub-array structure can significantly reduce the power consumption compared to the conventional “all-on” scheme.

This paper investigates a reconfigurable intelligent surface (RIS)-aided unmanned aerial vehicles (UAVs) system with non-orthogonal-multiple access (NOMA), where the transmit signals from multiple UAVs to ground users are strengthened through a RIS. An innovative framework is designed to minimize the total power consumption of the system, by jointly optimizing the position of UAVs, RIS reflection coefficients, active beamforming vectors and decoding order. To solve this problem, we first consider the sub-solution of the UAV’s location which can be achieved via the successive convex approximation (SCA) and maximum ratio transmission (MRT). By applying the Gaussian randomization procedure, we then yield the closed-form solution for RIS phase coefficients. Subsequently, the transmit power is obtained by the standard convex optimization methods. Finally, a dynamic-order decoding scheme is proposed to optimize the decoding order. Simulation results show that the resource allocation scheme can obviously reduce the total power consumption compared to the benchmark schemes.

In this paper, we consider the weighted sum of transferred power maximization under the secrecy rate (SR) constraints in a secure simultaneous wireless information and power transfer (SWIPT) communication network assisted by reconfigurable intelligent surfaces (RIS). To tackle this challenging problem, we combine the cutting-the-edge successive convex approximation (SCA) and penalty dual decomposition (PDD) methods and have successfully developed a novel iterative solution. Compared to the existing literature, our newly proposed algorithm can apply to the most generic system setting that has arbitrary number of information and/or energy receivers. Numerical results demonstrate the effectiveness of our proposed algorithm and the benefit of RIS deployment.

This paper investigates a novel intelligent reflecting surface (IRS)-based commensal symbiotic radio (CSR) system architecture consisting of a transmitter, an IRS, and an information receiver (IR). The primary transmitter communicates with the IR and at the same time assists the IRS in forwarding information to the IR. We formulate a bit error rate (BER) minimization problem by jointly optimizing the active beamformer at the base station and the phase shifts at the IRS, subject to a minimum primary rate requirement. Since the formulated optimization problem is non-convex with unit-modulus constraints, there are no standard convex techniques to solve it optimally in general. To tackle this difficulty, a penalty-based algorithm is proposed to obtain a high-quality solution, where semi-closed-form solutions for the active beamformer and the IRS phase shifts are derived based on Lagrange duality and Majorization-Minimization methods, respectively. Simulation results demonstrate the effectiveness of the proposed algorithm and show that the proposed CSR technique is able to achieve a lower BER than benchmark schemes.

The application of intelligent reflecting surface (IRS) into wireless powered mobile edge computing (WP-MEC) systems is investigated, where both time division multiple access (TDMA) and non-orthogonal multiple access (NOMA) schemes are considered for uplink (UL) offloading. We propose three different dynamic IRS beamforming (DIBF) schemes based on the flexibility for the IRS in adjusting its beamforming (BF) vector in each transmission frame. Under the DIBF framework, computation rate maximization problems are formulated for both the TDMA and NOMA schemes, respectively, by jointly optimizing the IRS BF and the resource allocation. An analytical comparison for the computation rate of TDMA and NOMA-based UL offloading schemes is provided. Finally, we propose computationally efficient algorithms to solve the corresponding computation rate maximization problems under the proposed DIBF framework. Numerical results unveil that the optimal time allocated to DL WPT can be effectively reduced with the aid of IRSs, which is beneficial for both the system’s spectral efficiency and energy efficiency.

This paper studies intelligent reflecting surface (IRS)-assisted wireless-powered communication networks (WPCNs), where a hybrid access point (HAP) broadcasts energy signals to multiple devices for their energy harvesting in the downlink (DL) and then the devices use the harvested energy to transmit information signals to the HAP in the uplink (UL) with the help of an IRS. We adopt a practical non-linear energy harvesting (EH) model and propose a power-splitting (PS) EH receiver architecture with multiple rectifiers to avoid the input radio-frequency power to get stuck into the saturation regime. To fully unleash the potential of IRS, we propose a dynamic IRS beamforming design, where the IRS phase-shift vectors vary across the durations of DL wireless energy transfer (WET) and UL wireless information transmission (WIT). The objective of this paper is to minimize the transmit energy consumption at the HAP by jointly optimizing the DL/UL time allocation, the HAP/devices transmit power, the PS factor, and IRS phase shifts, subject to a set of minimum throughput requirements for individual devices. To address the resulting non-convex optimization problem, an efficient alternating optimization based on the successive convex approximation (SCA) technique is proposed. Simulation results demonstrate the effectiveness of our proposed design over various benchmark schemes and also unveil the importance of the joint design of IRS beamforming and PS rectifiers for achieving energy efficient WPCNs in practice.

Reconfigurable intelligent surface (RIS) is a promising solution to adaptively manipulate wireless propagation with low-cost passive devices. However, the traditional passive RIS can offer sufficient signal strength only when receivers are very close to it. Moreover, the users at the back side of it cannot be well served due to its reflective property. In this paper we introduce a novel reflection and relay dual-functional RIS architecture, which can simultaneously realize passive reflection and active relay functionalities. The problem of joint transmit beamforming and dual-functional RIS design is investigated to maximize the achievable sum-rate of a multiuser multiple-input single-output (MU-MISO) system. Based on fractional programming (FP) theory and majorization-minimization (MM) technique, we propose an efficient iterative transmit beamforming and RIS design algorithm. Simulation results demonstrate the superiority of the introduced dual-functional RIS architecture and the effectiveness of the proposed algorithm.

Intelligent reflecting surface (IRS) is a promising technology for achieving spectrum and energy-efficient wireless networks cost-effectively. Most existing works on IRS have focused on exploiting IRS to enhance the performance of wireless communication or wireless information transmission (WIT), while its potential for boosting the efficiency of radio frequency (RF) wireless energy transmission (WET) still remains largely open. Although IRS-aided WET shares similar characteristics with IRS-aided WIT, they differ fundamentally in terms of design objective, receiver architecture, practical constraints, and so on. In this article, we provide a tutorial overview on how to efficiently design IRS-aided WET systems as well as IRS-aided systems with both WIT and WET, namely, IRS-aided simultaneous wireless information and power transfer (SWIPT) and IRS-aided wireless powered communication network (WPCN), from a communication and signal processing perspective. In particular, we present state-of-the-art solutions to tackle the unique challenges in operating these systems, such as IRS passive reflection optimization, channel estimation, and deployment. In addition, we propose new solution approaches and point out important directions for future research and investigation.

Intelligent reflecting surface (IRS) has been regarded as a promising and revolutionary technology for future wireless communication systems owing to its capability of tailoring signal propagation environment in an energy/spectrum/ hardware-efficient manner. However, most existing studies on IRS optimizations are based on a simple and ideal reflection model that is impractical in hardware implementation, which thus leads to severe performance loss in realistic wideband/multi-band systems. To deal with this problem, in this paper we first propose a more practical and more tractable IRS reflection model that describes the difference of reflection responses for signals at different frequencies. Then, we investigate the joint transmit beamforming and IRS reflection beamforming design for an IRS-assisted multi-cell multi-band system. Both power minimization and sum-rate maximization problems are solved by exploiting popular second-order cone programming (SOCP), Riemannian manifold, minimization-majorization (MM), weighted minimum mean square error (WMMSE), and block coordinate descent (BCD) methods. Simulation results illustrate the significant performance improvement of our proposed joint transmit beamforming and reflection design algorithms based on the practical reflection model in terms of power saving and rate enhancement.

This paper studies an intelligent reflecting surface (IRS)-assisted wireless-powered communication network (WPCN), where a hybrid access point (HAP) broadcasts energy signals to multiple devices for their energy harvesting in the downlink (DL) and then the devices use the harvested energy to transmit information signals to the HAP in the uplink (UL) with the help of an IRS. In particular, we propose three types of IRS beamforming configurations, namelyfully dynamic IRS beamforming (FDBF),partially dynamic IRS beamforming (PDBF), andstatic IRS beamforming (SBF), to strike a balance between the system performance and signaling overhead as well as implementation complexity. Moreover, we adopt a practical non-linear energy harvesting (EH) model, and leverage a power-splitting (PS) EH receiver architecture with multiple rectifiers to avoid the input radio frequency power to get stuck into the saturation regime. We aim to minimize the transmit energy consumption at the HAP by jointly optimizing the DL/UL time allocation, the HAP/devices transmit power, the PS factor, and IRS phase shifts, subject to a set of minimum throughput requirements for individual devices. To address the resulting non-convex optimization problems, a successive convex approximation (SCA) based alternating optimization algorithm is proposed. Moreover, we study the case with the ideal linear EH model and two algorithms, namely SCA-based algorithm and semidefinite relaxation (SDR) algorithm, are proposed. Simulation results demonstrate the effectiveness of our proposed designs over various benchmark schemes and also unveil the importance of the joint design of IRS beamforming and PS rectifiers for achieving energy efficient WPCNs in practice.

In this paper, a novel intelligent reflecting surface (IRS)-assisted wireless powered communication network (WPCN) architecture is proposed for power-constrained Internet-of-Things (IoT) smart devices, where IRS is exploited to improve the performance of WPCN under imperfect channel state information (CSI). We formulate a hybrid access point (HAP) transmit energy minimization problem by jointly optimizing time allocation, HAP energy beamforming, receiving beamforming, user transmit power allocation, IRS energy reflection coefficient and information reflection coefficient under the imperfect CSI and non-linear energy harvesting model. On account of the high coupling of optimization variables, the formulated problem is a non-convex optimization problem that is difficult to solve directly. To address the above-mentioned challenging problem, alternating optimization (AO) technique is applied to decouple the optimization variables to solve the problem. Specifically, through AO, time allocation, HAP energy beamforming, receiving beamforming, user transmit power allocation, IRS energy reflection coefficient and information reflection coefficient are divided into three sub-problems to be solved alternately. The difference-of-convex (DC) programming is used to solve the non-convex rank-one constraint in solving IRS energy reflection coefficient and information reflection coefficient. Numerical simulations verify the superiority of the proposed optimization algorithm in decreasing HAP transmit energy compared with other benchmark schemes.

Besides improving communication performance, intelligent reflecting surfaces (IRSs) are also promising enablers for achieving larger sensing coverage and enhanced sensing quality. Nevertheless, in the absence of a direct path between the base station (BS) and the targets, multi-target sensing is generally very difficult, since IRSs are incapable of proactively transmitting sensing beams or analyzing target information. Moreover, the echoes of different targets reflected via the IRS-assisted virtual links arrive at the BS from the same direction. In this paper, we study a wireless system comprising a multi-antenna BS and an IRS for multi-target sensing, where the beamforming vector and the IRS phase shifts are jointly optimized to improve the sensing performance. To meet the different sensing requirements, such as a minimum received power and a minimum sensing frequency, we propose three novel IRS-assisted sensing schemes: Time division (TD) sensing, signature sequence (SS) sensing, and hybrid TD-SS sensing. For TD sensing, the sensing tasks are performed in sequence over time. In contrast, the novel SS sensing scheme senses all targets simultaneously and establishes a relationship between the target directions and SSs. To strike a flexible balance between the beam pattern gain and sensing efficiency, we also propose a general hybrid TD-SS sensing scheme with target grouping, where targets belonging to the same group are sensed simultaneously via SS sensing, while the targets in different groups are assigned to orthogonal time slots. By controlling the number of groups, hybrid TD-SS sensing can provide a more flexible balance between beam pattern gain and sensing frequency. Moreover, we propose a two-layer penalty-based algorithm to solve the challenging non-convex optimization problem for the joint design of the BS beamformers, IRS phase shifts, and target grouping. Simulation results demonstrate the effectiveness of the proposed hybrid scheme in achieving a flexible trade-off between beam pattern gain and sensing frequency. Our results also reveal that the power leakage in unintended directions is larger for tighter interference constraints.

In this paper, we consider deploying an intelligent reflecting surface (IRS) to enhance the downlink (DL) energy transfer and uplink (UL) information transmission efficiency for secure multiple-input multiple-output (MIMO) wireless powered communication networks (WPCNs). We aim to maximize the secrecy throughput of all users by jointly optimizing the DL/UL time allocation, the energy transmit covariance matrix of hybrid access point (AP), the information transmit beamforming matrix of users and the phase shifts of IRS in DL/UL, subject to constraints of energy/information transmit power at the hybrid AP/users and that of unit-modulus IRS phase shifts for DL/UL. To tackle the non-convex problem, we first transform the original problem into an equivalent form based on the mean-square error (MSE) method given time allocation, and then apply the alternating algorithm to update the optimization variables iteratively. Specifically, the energy covariance matrix and the information beamforming matrix are obtained based on the dual subgradient method. For the IRS phase shifts, we investigate two IRS beamforming reflection setups, namely different DL/UL IRS beamforming and identical DL/UL IRS beamforming. For the former case, the second-order cone programming technique and the Majorization-Minimization algorithm/element by element iterative algorithm are applied to obtain the DL and UL IRS phase shifts, respectively. For the latter case, the IRS phase shifts are obtained by the successive convex approximation technique. To further reduce the computational complexity of the single-user system, we derive the closed-form solutions of IRS phase shifts in each iteration for the two different reflection setups. Simulation results show that all the proposed algorithms can greatly improve the secrecy throughput compared to the conventional system without IRS.

Unmanned aerial vehicle (UAV) is a promising communication platform to assist terrestrial networks. In this work, we aim to provide relay communication to the blocked or low-quality terrestrial networks via an aerial relay. Nevertheless, major issues of the considered system are the worrying security and limited service time. Thus, we study a novel aerial relay system via collaborative beamforming (CB) by exploiting a UAV-enabled virtual antenna array (UVAA) to achieve a secure and energy-efficient communication for remote ground users (GUs). Specifically, we formulate a secure and energy-efficient communication multi-objective optimization problem (SECMOP) to circumvent the effects of the known and unknown eavesdroppers and minimize the propulsion energy consumption of UAVs, by optimizing the hovering positions and excitation current weights of UAVs and the scheduling for communicating with the remote GUs. The formulated SECMOP is challenging and proven to be NP-hard. Thus, we propose an improved evolutionary computation method with several enhanced designs to solve this problem. Simulation results demonstrate the benefits of the proposed IMODAOM against various benchmark algorithms. Moreover, we find that the UVAA-based relay can achieve substantial energy consumption reduction as compared to the multi-hop relay scheme.

The energy efficiency (EE) of femtocells is always limited by the surrounding radio environments in heterogeneous networks (HetNets), such as walls and obstacles. In this paper, we propose to deploy reconfigurable intelligent surfaces (RISs) to improve the EE of femtocells. However, perfect channel state information is more difficult to obtain due to the passive characteristics of RISs and non-cooperative relationship between different tiers. Besides, the low-cost transceivers and reflecting units suffer nontrivial hardware impairments (HWIs) due to the hardware limitations of practical systems. To this end, we investigate a realistic robust beamforming design based on max-min fairness for an RIS-aided HetNet under channel uncertainties and residual HWIs. The joint optimization of transmit beamforming vectors of femto base stations (FBSs) and the phase-shift matrices of RISs is formulated as a non-convex problem to maximize the minimum EE of the femtocell subject to the constraints of the maximum transmit power of FBSs, the quality of service of users, and unit modulus phase-shift constraints of RISs. We develop an iterative block coordinate descent-based algorithm which exploits the semi-definite relaxation, the S-procedure, and the singular value decomposition method. Simulation results reveal that the proposed algorithm outperforms existing algorithms in terms of fairness, EE, and outage probability.

This paper investigates the security enhancement of an intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) network, where a base station (BS) serves users securely with the assistance of distributed IRSs. Considering that eavesdropper’s instantaneous channel state information (CSI) is challenging to acquire in practice, we utilize secrecy outage probability (SOP) as the security metric. A problem of maximizing the minimum secrecy rate among users, by jointly optimizing transmit beamforming at the BS and phase shifts of the IRSs, is formulated. For a special case with a single-antenna BS, we derive the closed-form SOP expressions and propose a novelring-penaltybased successive convex approximation (SCA) algorithm to design transmit power and phase shifts jointly. For a general multi-antenna BS case, we develop a Bernstein-type inequality based alternating optimization (AO) algorithm to solve the challenging problem. Numerical results demonstrate the advantages of the proposed algorithms over the baseline schemes. The results also show that: 1) the maximum secrecy rate is achieved when distributed IRSs share the reflecting elements equally; and 2) the distributed IRS deployment does not always outperform the centralized IRS deployment, due to the tradeoff between the number of IRSs and the reflecting elements equipped at each IRS.

In this paper, we consider an intelligent reflecting surface (IRS)-assisted cognitive radio system and maximize the secondary user (SU) rate by jointly optimizing the transmit power of secondary transmitter (ST) and the IRS’s reflect beamforming, subject to the constraints of the minimum required signal-to-interference-plus-noise ratio at the primary receiver, the ST’s maximum transmit power, and the unit modulus of the IRS reflect beamforming vector. This joint optimization problem can be solved suboptimally by the non-convex optimization techniques, which however usually require complicated mathematical transformations and are computationally intensive. To address this challenge, we propose an algorithm based on the deep deterministic policy gradient (DDPG) method. To achieve a higher learning efficiency and a lower reward variance, we propose another algorithm based on the soft actor-critic (SAC) method. In these proposed algorithms, a reward impact adjustment approach is proposed to improve their learning efficiency and stability. Simulation results show that the two proposed algorithms can achieve comparable SU rate performance with much shorter running time, as compared to the existing non-convex optimization-based benchmark algorithm, and that the proposed SAC-based algorithm learns faster and achieves a higher average reward with lower variance, as compared to the proposed DDPG-based algorithm.

Channel estimation is a practical challenge for intelligent reflecting surface (IRS) aided wireless communication. As the number of IRS reflecting elements or IRS-aided users increases, the channel training overhead becomes excessively high, which results in long delay and low throughput in data transmission. To tackle this challenge, we propose in this paper a new anchor-assisted channel estimation approach, where two anchor nodes, namely A1 and A2, are deployed near the IRS for facilitating its aided base station (BS) in acquiring the cascaded BS-IRS-user channels required for data transmission. Specifically, in the first scheme, the partial channel state information (CSI) on the element-wise channel gain square of the common BS-IRS link for all users is first obtained at the BS via the anchor-assisted training and feedback. Then, by leveraging such partial CSI, the cascaded BS-IRS-user channels are efficiently resolved at the BS with additional training by the users. While in the second scheme, the BS-IRS-A1 and A1-IRS-A2 channels are first estimated via the training by A1. Then, with additional training by A2, all users estimate their individual cascaded A2-IRS-user channels simultaneously. Based on the CSI fed back from A2 and all users, the BS resolves the cascaded BS-IRS-user channels efficiently. In both schemes, the channels among the fixed BS, IRS, and two anchors are estimated in a large timescale, which greatly reduces the real-time training overhead. Simulation results demonstrate that our proposed anchor-assisted channel estimation schemes achieve superior performance as compared to existing IRS channel estimation schemes, under various practical setups. In addition, the first proposed scheme outperforms the second one when the number of antennas at the BS is sufficiently large, and vice versa.

This paper studies intelligent reflecting surface (IRS)-aided full-duplex (FD) wireless-powered communication network (WPCN), where a hybrid access point (HAP) broadcasts energy signals to multiple devices for their energy harvesting in the downlink (DL) and meanwhile receives information signals in the uplink (UL) with the help of IRS. Particularly, we propose three types of IRS beamforming configurations to strike a balance between the system performance and signaling overhead as well as implementation complexity. We first propose thefully dynamic IRS beamforming, where the IRS phase-shift vectors vary with each time slot for both DL wireless energy transfer (WET) and UL wireless information transmission (WIT). To further reduce signaling overhead and implementation complexity, we then study two special cases, namely,partially dynamic IRS beamformingandstatic IRS beamforming. For the former case, two different phase-shift vectors can be exploited for the DL WET and the UL WIT, respectively, whereas for the latter case, the same phase-shift vector needs to be applied for both DL and UL transmissions. We aim to maximize the system throughput by jointly optimizing the time allocation, HAP transmit power, and IRS phase shifts for the above three cases. Two efficient algorithms based on alternating optimization and penalty-based algorithms are respectively proposed for both perfect self-interference cancellation (SIC) case and imperfect SIC case by applying successive convex approximation and difference-of-convex optimization techniques. Simulation results demonstrate the benefits of IRS for enhancing the performance of FD-WPCN, especially with fully dynamic IRS beamforming, and also show that the IRS-aided FD-WPCN is able to achieve significantly performance gain compared to its counterpart with half-duplex when the self-interference (SI) is properly suppressed.

This paper proposes a novel network framework of intelligent reflecting surface (IRS)-assisted simultaneous wireless information and power transfer (SWIPT) non-orthogonal multiple access (NOMA) networks, where IRS is used to enhance the NOMA performance and the wireless power transfer (WPT) efficiency of SWIPT. We formulate a problem of minimizing base station (BS) transmit power by jointly optimizing successive interference cancellation (SIC) decoding order, BS transmit beamforming vector, power splitting (PS) ratio and IRS phase shift while taking into account the quality-of-service (QoS) requirement and energy harvested threshold of each user. The formulated problem is non-convex optimization problem, which is difficult to solve it directly. Hence, a two-stage algorithm is proposed to solve the above-mentioned problem by applying semidefinite relaxation (SDR), Gaussian randomization and successive convex approximation (SCA). Specifically, after determining SIC decoding order by designing IRS phase shift in the first stage, we alternately optimize BS transmit beamforming vector, PS ratio, and IRS phase shift to minimize the BS transmit power. Numerical results validate the effectiveness of our proposed optimization algorithm in reducing BS transmit power compared to other baseline algorithms. Meanwhile, compared with non-IRS-assisted network, the IRS-assisted SWIPT NOMA network can decrease BS transmit power by 51.13%.

Power saving and battery-life extension have always been a critical concern for IoT network deployment. One effective solution is to switch wireless devices into sleep mode to save power. This paper considers the power control in an IoT network via jointly activating IoT sensors and designing their transmit beamforming. Besides, inspired by the great potential of reconfigurable intelligent surface (RIS) in energy saving, we additionally introduce RIS to further lower the sensors’ power consumption. The considered problem is highly challenging due to its combinatorial nature, the highly non-convex quality-of-service (QoS) constraint and the hardware restrictions from the RIS. By exploiting the cutting-the-edge majorization minimization (MM) and the penalty dual decomposition (PDD) frameworks, we have successfully developed highly efficient solutions to tackle this problem. Our proposed solutions can achieve nearly identical performance with that of the exhaustive search but with a much lower complexity. Besides, as revealed by the numerical experiments, our proposed sensor activation scheme can switch off a large portion of sensors under mild QoS requirements, which significantly reduces power expenditure. Moreover, the deployment of RIS can bring an additional 45% – 70% power saving compared to the no-RIS case.

In this paper, we investigate covert communication in an intelligent reflecting surface (IRS)-assisted non-orthogonal multiple access (NOMA) system, where a legitimate transmitter (Alice) applies NOMA for downlink and uplink transmissions with a covert user (Bob) and a public user (Roy) aided by an IRS. Specifically, we propose new IRS-assisted downlink and uplink NOMA schemes to hide the existence of Bob’s covert transmission from a warden (Willie), which cost-effectively exploits the phase-shift uncertainty of the IRS and the non-orthogonal signal transmission of Roy as the cover medium without requiring additional uncertainty sources. Assuming the worst-case covert communication scenario where Willie can optimally adjust the detection threshold for his detector, we derive an analytical expression for the minimum average detection error probability of Willie achieved by each of the proposed schemes. To further enhance the covert communication performance, we propose to maximize the covert rates of Bob by jointly optimizing the transmit power and the IRS reflect beamforming, subject to given requirements on the covertness against Willie and the quality-of-service at Roy. Simulation results demonstrate the covertness advantage of the proposed schemes and confirm the accuracy of the derived analytical results. Interestingly, it is found that covert communication is impossible without using IRS or NOMA for the considered setup while the proposed schemes can always guarantee positive covert rates.

This paper investigates the task offloading problem in a hybrid intelligent reflecting surface (IRS) and massive multiple-input multiple-output (MIMO) relay assisted fog computing system, where multiple task nodes (TNs) offload their computational tasks to computing nodes (CNs) nearby massive MIMO relay node (MRN) and fog access node (FAN) via the IRS for execution. By considering the practical imperfect channel state information (CSI) model, we formulate a joint task offloading, IRS phase shift optimization, and power allocation problem to minimize the total energy consumption. We solve the resultant non-convex optimization problem in three steps. First, we solve the IRS phase shift optimization problem with the sequential rank-one constraint relaxation (SROCR) algorithm and semidefinite relaxation (SDR) algorithm for a given power- and computational resource allocation. Then, we exploit a differential convex (DC) optimization framework to determine the power allocation decision that minimizes the total energy consumption. Given the IRS phase shifts, the computational resources, and the power allocation, we propose an alternating optimization algorithm for finding the jointly optimized results. The simulation results demonstrate the effectiveness of the proposed scheme as compared with other benchmark schemes, and the energy efficient offloading strategy for the proposed fog computing system can be chosen according to the asymptotic form of the effective signal-to-interference-plus-noise ratio (SINR).

In this paper, we propose anew dynamic IRS beamformingframework to boost the sum throughput of an intelligent reflecting surface (IRS) aided wireless powered communication network (WPCN). Specifically, the IRS phase-shift vectors across time and resource allocation are jointly optimized to enhance the efficiencies of both downlink wireless power transfer (DL WPT) and uplink wireless information transmission (UL WIT) between a hybrid access point (HAP) and multiple wirelessly powered devices. To this end, we first study three special cases of the dynamic IRS beamforming, namelyuser-adaptiveIRS beamforming,UL-adaptiveIRS beamforming, andstatic IRS beamforming, by characterizing their optimal performance relationships and proposing corresponding algorithms. Interestingly, it is rigorously proved that the latter two cases achieve the same throughput, thus helping halve the number of IRS phase shifts to be optimized and signalling overhead practically required for UL-adaptive IRS beamforming. Then, we propose a general optimization framework for dynamic IRS beamforming, which is applicable for any given number of IRS phase-shift vectors available. Despite of the non-convexity of the general problem with highly coupled optimization variables, we propose two algorithms to solve it and particularly, the low-complexity algorithm exploits the intrinsic structure of the optimal solution as well as the solutions to the cases with user-adaptive and static IRS beamforming. Simulation results validate our theoretical findings, illustrate the practical significance of IRS with dynamic beamforming for spectral and energy efficient WPCNs, and demonstrate the effectiveness of our proposed designs over various benchmark schemes.

This paper investigates the problem of distributed resource management in two-tier heterogeneous networks, where each cell selects its joint device association, spectrum allocation, and power allocation strategy based only on locally-observed information without any central controller. As the optimization problem with devices’ quality-of-service (QoS) constraints is non-convex and NP-hard, we model it as a Markov decision process (MDP). Considering the fact that the network is highly complex with large state and action spaces, a multi-agent dueling deep-Q network-based algorithm combined with distributed coordinated learning is proposed to effectively learn the optimized intelligent resource management policy, where the algorithm adopts dueling deep network to learn the action-value distribution by estimating both the state-value and action advantage functions. Under the distributed coordinated learning manner and dueling architecture, the learning algorithm can rapidly converge to the optimized policy. Simulation results demonstrate that the proposed distributed coordinated learning algorithm outperforms other existing learning algorithms in terms of learning efficiency, network data rate, and QoS satisfaction probability.

This paper investigates the secure transmission in a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted uplink non-orthogonal multiple access system, where the legitimate users send confidential signals to the base station by exploiting STAR-RIS to reconfigure the electromagnetic propagation environment proactively. Depending on the availability of the eavesdropping channel state information (CSI), both the full CSI and statistical CSI of the eavesdropper are considered. For the full eavesdropping CSI scenario, we adopt the adaptive-rate wiretap code scheme with the aim of maximizing minimum secrecy capacity subject to the successive interference cancellation decoding order constraints. To proceed, we propose an alternating hybrid beamforming (AHB) algorithm to jointly optimize the receive beamforming, transmit power, and reflection/transmission coefficients. While for the statistical eavesdropping CSI scenario, the constant-rate wiretap code scheme is employed to minimize the maximum secrecy outage probability (SOP) subject to the quality-of-service requirements of legitimate users. Then, we derive the exact SOP expression under the constant-rate coding strategy and develop an extended AHB algorithm for the joint secrecy beamforming design. Simulation results demonstrate the effectiveness of the proposed scheme. Moreover, some useful guidance about the quantification of phase shift/amplitude and the deployment of STAR-RIS is provided.

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

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

The system capacity can be remarkably enhanced with the help of intelligent reflecting surface (IRS) which has been recognized as a advanced breaking point for the beyond fifth-generation (B5G) communications. However, the accuracy of IRS channel estimation restricts the potential of IRS-assisted multiple input multiple output (MIMO) systems. Especially, for the resource-limited indoor applications which typically contains lots of parameters estimation calculation and is limited by the rare pilots, the practical applications encountered severe obstacles. Previous works takes the advantages of mathematical-based statistical approaches to associate the optimization issue, but the increasing of scatterers number reduces the practicality of statistical approaches in more complex situations. To obtain the accurate estimation of indoor channels with appropriate piloting overhead, an offset learning (OL)-based neural network method is proposed. The proposed estimation method can trace the channel state information (CSI) dynamically with non-prior information, which get rid of the IRS-assisted channel structure as well as indoor statistics. Moreover, a convolution neural network (CNN)-based inversion is investigated. The CNN, which owns powerful information extraction capability, is deployed to estimate the offset, it works as an offset estimation operator. Numerical results show that the proposed OL-based estimator can achieve more accurate indoor CSI with a lower complexity as compared to the benchmark schemes.

This paper studies intelligent reflecting surface (IRS)-aided full-duplex (FD) wireless-powered communication network (WPCN), where a hybrid access point (HAP) broadcasts energy signals to multiple devices for their energy harvesting in the downlink (DL) and meanwhile receives information signals in the uplink (UL) with the help of IRS. We propose a fully dynamic IRS beamforming design, where the IRS phase-shift vectors vary with each time slot for both DL wireless energy transfer (WET) and UL wireless information transmission (WIT). We aim to maximize the system throughput by jointly optimizing the time allocation, HAP transmit power, and IRS phase shifts. Since the formulated problem is non-convex due to the highly coupled optimization variables in the objective function and non-convex unit-modulus constraints of phase shifts, we propose a novel penalty-based algorithm consisting of a two-layer iteration, i.e., an inner layer iteration and an outer layer iteration. Specifically, the inner layer solves the penalized optimization problem, while the outer layer updates the penalty coefficient over iterations to guarantee convergence. Simulation results demonstrate that integrating IRS into WPCN significantly improve the system throughput and also unveil that the IRS-aided FD-WPCN is particularly beneficial for the large number of devices scenario.

This paper investigates the task offloading problem in a hybrid intelligent reflecting surface (IRS) and massive multiple-input multiple-output (MIMO) relay assisted fog computing system, where multiple task nodes (TNs) offload their computational tasks to computing nodes (CNs) nearby massive MIMO relay node (MRN) and fog access node (FAN) via the IRS for execution. By considering the practical imperfect channel state information (CSI) model, we formulate a joint task offloading, IRS phase shift optimization, and power allocation problem to minimize the total energy consumption. We solve the resultant non-convex optimization problem in three steps. First, we solve the IRS phase shift optimization problem with the semidefinite relaxation (SDR) algorithm. Then, we exploit a differential convex (DC) optimization framework to determine the power allocation decision. Given the IRS phase shifts, the computational resources, and the power allocation, we propose an alternating optimization algorithm for finding the jointly optimized results. The simulation results demonstrate the effectiveness of the proposed scheme as compared with other benchmark schemes.

This paper investigates the security enhancement of an intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) network, where a distributed IRS enabled NOMA transmission framework is proposed to serve users securely in the presence of a passive eavesdropper. Considering that the instantaneous channel state information (CSI) of the eavesdropper is challenging to acquire in practice, we utilize the secrecy outage probability (SOP) as the security metric. A problem by jointly optimizing the transmit power at the base station (BS) and reflection phase shifts at IRSs, subject to the successive interference cancellation (SIC) decoding constraints and SOP constraints, is formulated to maximize the minimum secrecy rate among legitimate users. To tackle the non-convex problem, we first derive the exact SOP in closed-form expressions and then propose a novel ring-penalty based successive convex approximation (SCA) algorithm to design power allocation and phase shifts jointly. Numerical results validate the convergence and the secrecy superiority of proposed scheme over the baseline schemes.

Internet of things (IoT) technology is an essential enabler to realize ubiquitous connections and pervasive intelli-gence for the future wireless communication system. The energy self-sustainability based on the wireless power transfer technique and the coexistence with heterogeneous networks will become two predominant attributes of IoT networks. In this paper we consider the system design in a context of coexistence of a wireless powered sensor network and a cellular system, both of which share common spectrum bandwidth and are assisted by intelligent reflecting surface (IRS). Specifically, the wireless sensors exploit the harvested energy from the cellular base station (BS) to transfer information to a data sink. We aim to design a cooperation scheme via jointly optimizing the time allocation of channel use, collaborative beamforming across networks and IRS phase-shifting control to improve the sensing network's throughput while guaranteeing the cellular users' quality of service. This design problem leads to a highly nonconvex and difficult mathematical optimization problem. Via utilizing the penalty-duality-decomposition (PDD) and successive convex approximation (SCA) methods, we have managed to develop an alternative optimization solution. Nu-merical results verify the effectiveness of our algorithm and demonstrate the benefits that come from the cooperative network design.

This paper investigates the problem of distributed resource management (i.e., joint device association, spectrum allocation, and power allocation) in two-tier heterogeneous networks without any central controller. Considering the fact that the network is highly complex with large state and action spaces, a multi-agent dueling deep-Q network-based algorithm combined with distributed coordinated learning is proposed to effectively learn the optimized intelligent resource management policy, where the algorithm adopts dueling deep network to learn the action-value distribution by estimating both the state-value and action advantage functions. Under the distributed coordinated learning manner and dueling architecture, the learning algorithm can rapidly converge to the optimized policy. Simulation results demonstrate that the proposed distributed coordinated learning algorithm outperforms other existing learning algorithms in terms of learning efficiency, network data rate, and QoS satisfaction probability.

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

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

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

Developing resource allocation algorithms for the uplink sparse code multiple access (SCMA) scheme to satisfy multiple objectives is challenging, especially where users are randomly distributed. In this paper, we aim to address this challenge by developing a joint resource allocation method as a multi-objective optimization (MO) problem to maximize the average sum rate and fairness among users as key and sub-key objectives, respectively. For this purpose, the exact analytical expressions for the average sum rate and users' individual rate are extracted based on an adaptive weighted graph matrix (AWGM). An AWGM matrix beneficially replaces the factor graph and the power allocation matrices to simplify the MO problem based on the asymmetric modified bipartite matching (AMBM) algorithm. The power allocation strategy is utilized during the optimal resource assignment process using the AMBM algorithm. After the AMBM process, we propose a low-complexity four-step algorithm to obtain the AWGM. The simulation results show that our proposed method can compromise and improve the multiple objectives' performance and guarantees a stable range of network performance at different times.

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

Intelligent reflecting surface (IRS) is envisioned as a revolutionary technology for future wireless communication systems since it can intelligently change radio environment and integrate it into wireless communication optimization. However, most existing works adopted an ideal IRS reflection model, which is impractical and can cause significant performance degradation in realistic wideband systems. To address this issue, we first study the dual phase- and amplitude-squint effect of reflected signals and present a simplified practical IRS reflection model for wideband signals. Then, an IRS enhanced wideband multiuser multi-input single-output orthogonal frequency division multiplexing (MU-MISO-OFDM) system is investigated. We aim to jointly design the transmit beamformer and IRS reflection for the case of using both continuous and discrete phase shifters to maximize the average sum-rate over all subcarriers. By exploiting the relationship between sum-rate maximization and mean square error (MSE) minimization, the original problem is equivalently transformed into a multi-block/variable problem, which can be efficiently solved by the block coordinate descent (BCD) method. Complexity and convergence for both cases are analyzed or illustrated. Simulation results demonstrate that the proposed algorithm can offer significant average sum-rate enhancement compared to that achieved using the ideal IRS reflection model, which confirms the importance of the use of the practical model for the design of wideband systems.

In this paper, we consider the weighted sum-power minimization under quality-of-service (QoS) constraints in the multi-user multi-input-single-output (MISO) uplink wireless network assisted by intelligent reflecting surface (IRS). We perform a comprehensive investigation on various aspects of this problem. First, when users have sufficient transmit powers, we present a new sufficient condition guaranteeing arbitrary information rate constraints. This result strengthens the feasibility condition in existing literature. Then, we design novel penalty dual decomposition (PDD) based and nonlinear equality constrained alternative direction method of multipliers (neADMM) based solutions to tackle the IRS-dependent-QoS-constraints, which effectively solve the feasibility check and power minimization problems. Besides, we further extend our proposals to the cases where channel status information (CSI) is imperfect and develop an online stochastic algorithm that satisfy QoS constraints stochastically without requiring prior knowledge of CSI errors. Extensive numerical results are presented to verify the effectiveness of our proposed algorithms.

Intelligent reflecting surface (IRS) is an enabling technology to engineer the radio signal propagation in wireless networks. By smartly tuning the signal reflection via a large number of low-cost passive reflecting elements, IRS is capable of dynamically altering wireless channels to enhance the communication performance. It is thus expected that the new IRS-aided hybrid wireless network comprising both active and passive components will be highly promising to achieve a sustainable capacity growth cost-effectively in the future. Despite its great potential, IRS faces new challenges to be efficiently integrated into wireless networks, such as reflection optimization, channel estimation, and deployment from communication design perspectives. In this paper, we provide a tutorial overview of IRS-aided wireless communications to address the above issues, and elaborate its reflection and channel models, hardware architecture and practical constraints, as well as various appealing applications in wireless networks. Moreover, we highlight important directions worthy of further investigation in future work.

Intelligent reflecting surface (IRS) is a promising new paradigm to achieve high spectral and energy efficiency for future wireless networks by reconfiguring the wireless signal propagation via passive reflection. To reap the promising gains of IRS, channel state information (CSI) is essential, whereas channel estimation errors are inevitable in practice due to limited channel training resources. In this paper, in order to optimize the performance of IRS-aided multiuser communications with imperfect CSI, we propose to jointly design the active transmit precoding at the access point (AP) and passive reflection coefficients of the IRS, each consisting of not only the conventional phase shift and also the newly exploited amplitude variation. First, the achievable rate of each user is derived assuming a practical IRS channel estimation method, which shows that the interference due to CSI errors is intricately related to the AP transmit precoders, the channel training power and the IRS reflection coefficients during both channel training and data transmission. Next, for the single-user case, by combining the benefits of the penalty method, Dinkelbach method and block successive upper-bound minimization (BSUM) method, a new penalized Dinkelbach-BSUM algorithm is proposed to optimize the IRS reflection coefficients for maximizing the achievable data transmission rate subjected to CSI errors; while for the multiuser case, a new penalty dual decomposition (PDD)-based algorithm is proposed to maximize the users' weighted sum-rate. Finally, simulation results are presented to validate the effectiveness of our proposed algorithms as compared to benchmark schemes. In particular, useful insights are drawn to characterize the effect of IRS reflection amplitude control (with/without the conventional phase-shift control) on the system performance under imperfect CSI.

Thanks to their quick placement and high flexibility, unmanned aerial vehicles (UAVs) can be very useful in the current and future wireless communication systems. With a growing number of smart devices and infrastructure-free communication networks, it is necessary to legitimately monitor these networks to prevent crimes. In this paper, a novel framework is proposed to exploit the flexibility of the UAV for legitimate monitoring via joint trajectory design and energy management. The system includes a suspicious transmission link with a terrestrial transmitter and a terrestrial receiver, and a UAV to monitor the suspicious link. The UAV can adjust its positions and send jamming signal to the suspicious receiver to ensure successful eavesdropping. Based on this model, we first develop an approach to minimize the overall jamming energy consumption of the UAV. Building on a judicious (re-)formulation, an alternating optimization approach is developed to compute a locally optimal solution in polynomial time. Furthermore, we model and include the propulsion power to minimize the overall energy consumption of the UAV. Leveraging the successive convex approximation method, an effective iterative approach is developed to find a feasible solution fulfilling the Karush-Kuhn-Tucker (KKT) conditions. Extensive numerical results are provided to verify the merits of the proposed 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.

Malicious jamming launched by smart jammers can attack legitimate transmissions, which has been regarded as one of the critical security challenges in wireless communications. With this focus, this paper considers the use of an intelligent reflecting surface (IRS) to enhance anti-jamming communication performance and mitigate jamming interference by adjusting the surface reflecting elements at the IRS. Aiming to enhance the communication performance against a smart jammer, an optimization problem for jointly optimizing power allocation at the base station (BS) and reflecting beamforming at the IRS is formulated while considering quality of service (QoS) requirements of legitimate users. As the jamming model and jamming behavior are dynamic and unknown, a fuzzy win or learn fast-policy hill-climbing (WoLF-CPHC) learning approach is proposed to jointly optimize the anti-jamming power allocation and reflecting beamforming strategy, where WoLF-CPHC is capable of quickly achieving the optimal policy without the knowledge of the jamming model, and fuzzy state aggregation can represent the uncertain environment states as aggregate states. Simulation results demonstrate that the proposed anti-jamming learning-based approach can efficiently improve both the IRS-assisted system rate and transmission protection level compared with existing solutions.

In this paper, we study an intelligent reflecting surface (IRS)-aided wireless secure communication system, where an IRS is deployed to adjust its reflecting elements to secure the communication of multiple legitimate users in the presence of multiple eavesdroppers. Aiming to improve the system secrecy rate, a design problem for jointly optimizing the base station (BS)'s beamforming and the IRS's reflecting beamforming is formulated considering different quality of service (QoS) requirements and time-varying channel conditions. As the system is highly dynamic and complex, and it is challenging to address the non-convex optimization problem, a novel deep reinforcement learning (DRL)-based secure beamforming approach is firstly proposed to achieve the optimal beamforming policy against eavesdroppers in dynamic environments. Furthermore, post-decision state (PDS) and prioritized experience replay (PER) schemes are utilized to enhance the learning efficiency and secrecy performance. Specifically, a modified PDS scheme is presented to trace the channel dynamic and adjust the beamforming policy against channel uncertainty accordingly. Simulation results demonstrate that the proposed deep PDS-PER learning based secure beamforming approach can significantly improve the system secrecy rate and QoS satisfaction probability in IRS-aided secure communication systems.

Intelligent reflecting surface (IRS) has drawn a lot of attention recently as a promising new solution to achieve high spectral and energy efficiency for future wireless networks. By utilizing massive low-cost passive reflecting elements, the wireless propagation environment becomes controllable and thus can be made favorable for improving the communication performance. Prior works on IRS mainly rely on the instantaneous channel state information (I-CSI), which, however, is practically difficult to obtain for IRS-associated links due to its passive operation and large number of reflecting elements. To overcome this difficulty, we propose in this paper a new two-timescale (TTS) transmission protocol to maximize the achievable average sum-rate for an IRS-aided multiuser system under the general correlated Rician channel model. Specifically, the passive IRS phase shifts are first optimized based on the statistical CSI (S-CSI) of all links, which varies much slowly as compared to their I-CSI; while the transmit beamforming/precoding vectors at the access point (AP) are then designed to cater to the I-CSI of the users' effective fading channels with the optimized IRS phase shifts, thus significantly reducing the channel training overhead and passive beamforming design complexity over the existing schemes based on the I-CSI of all channels. Besides, for ease of practical implementation, we consider discrete phase shifts at each reflecting element of the IRS. For the single-user case, an efficient penalty dual decomposition (PDD)-based algorithm is proposed, where the IRS phase shifts are updated in parallel to reduce the computational time. For the multiuser case, we propose a general TTS stochastic successive convex approximation (SSCA) algorithm by constructing a quadratic surrogate of the objective function, which cannot be explicitly expressed in closed-form. Simulation results are presented to validate the effectiveness of our proposed algorithms and evaluate the impact of S-CSI and channel correlation on the system performance.

Due to the passive nature of Intelligent Reflecting Surface (IRS), channel estimation is a fundamental challenge in IRS-aided wireless networks. Particularly, as the number of IRS reflecting elements and/or that of IRS-served users increase, the channel training overhead becomes excessively high. To tackle this challenge, we propose in this paper a new anchor-assisted two-phase channel estimation scheme, where two anchor nodes, namely A1 and A2, are deployed near the IRS for helping the base station (BS) to acquire the cascaded BS-IRS-user channels. Specifically, in the first phase, the partial channel state information (CSI), i.e., the element-wise channel gain square, of the BS-IRS link is obtained by estimating the BS-IRS-A1/A2 channels and the A1-IRS-A2 channel, separately. Then, in the second phase, by leveraging such partial knowledge of the BS-IRS channel that is common to all users, the individual cascaded BS-IRS-user channels are efficiently estimated. Simulation results demonstrate that the proposed anchor-assisted channel estimation scheme is able to achieve comparable mean-squared error (MSE) performance as compared to the conventional scheme, but with significantly reduced channel training time.

Sparse code multiple access (SCMA) is one of the promising candidates for new radio access interface. The new generation communication system is expected to support massive user access with high capacity. However, there are numerous problems and barriers to achieve optimal performance, e.g., the multiuser interference and high power consumption. In this paper, we present optimization methods to enhance the spectral and energy efficiency for SCMA with individual rate requirements. The proposed method has shown a better network mapping matrix based on power allocation and codebook assignment. Moreover, the proposed method is compared with orthogonal frequency division multiple access (OFDMA) and code division multiple access (CDMA) in terms of spectral efficiency (SE) and energy efficiency (EE) respectively. Simulation results show that SCMA performs better than OFDMA and CDMA both in SE and EE.

Malicious jamming launched by smart jammer, which attacks legitimate transmissions has been regarded as one of the critical security challenges in wireless communications. Thus, this paper exploits intelligent reflecting surface (IRS) to enhance anti-jamming communication performance and mitigate jamming interference by adjusting the surface reflecting elements at the IRS. Aiming to enhance the communication performance against smart jammer, an optimization problem for jointly optimizing power allocation at the base station (BS) and reflecting beamforming at the IRS is formulated. As the jamming model and jamming behavior are dynamic and unknown, a win or learn fast policy hill-climbing (WoLFCPHC) learning approach is proposed to jointly optimize the anti-jamming power allocation and reflecting beamforming strategy without the knowledge of the jamming model. Simulation results demonstrate that the proposed anti-jamming based-learning approach can efficiently improve both the the IRS-assisted system rate and transmission protection level compared with existing solutions.

In this paper, we study an intelligent reflecting surface (IRS)-aided wireless secure communication system for physical layer security, where an IRS is deployed to adjust its reflecting elements to secure the communication of multiple legitimate users in the presence of multiple eavesdroppers. Aiming to improve the system secrecy rate, a design problem for jointly optimizing the base station (BS)'s beamforming and the IRS's reflecting beamforming is formulated considering different quality of service (QoS) requirements and time-varying channel conditions. As the system is highly dynamic and complex, a novel deep reinforcement learning (DRL)-based secure beamforming approach is firstly proposed to achieve the optimal beamforming policy against eavesdroppers in dynamic environments. Simulation results demonstrate that the proposed deep learning based secure beamforming approach can significantly improve the system secrecy performance compared with other approaches.

Intelligent reflecting surface (IRS) is a promising new paradigm to achieve high spectral and energy efficiency for future wireless networks by reconfiguring the wireless signal propagation via passive reflection. To reap the potential gains of IRS, channel state information (CSI) is essential, whereas channel estimation errors are inevitable in practice due to limited channel training resources. In this paper, in order to optimize the performance of IRS-aided communications with imperfect CSI, we propose to jointly design the active transmit precoding at the access point (AP) and passive reflection coefficients of IRS, each consisting of not only the conventional phase shift and also the newly exploited amplitude variation. First, the user's achievable rate is derived assuming a practical IRS channel estimation method, which shows that the interference due to CSI errors is intricately related to the AP transmit precoder, the channel training power and the IRS reflection coefficients during both channel training and data transmission. Next, by combining the benefits of the penalty method, Dinkelbach method and block successive upper-bound minimization (BSUM) method, a new penalized Dinkelbach-BSUM algorithm is proposed to optimize the IRS reflection coefficients for maximizing the achievable data transmission rate subjected to CSI errors. Finally, simulation results are presented to validate the effectiveness of our proposed algorithm as compared to benchmark schemes. In particular, useful insights are drawn to characterize the effect of IRS reflection amplitude control (with/without the conventional phase-shift control) on the system performance under imperfect CSI.

Intelligent reflecting surface (IRS) is a new and revolutionizing technology for achieving spectrum and energy efficient wireless networks. By leveraging massive low-cost passive elements that are able to reflect radio-frequency (RF) signals with adjustable phase shifts, IRS can achieve high passive beamforming gains, which are particularly appealing for improving the efficiency of RF-based wireless power transfer. Motivated by the above, we study in this paper an IRS-assisted simultaneous wireless information and power transfer (SWIPT) system. Specifically, a set of IRSs are deployed to assist in the information/power transfer from a multi-antenna access point (AP) to multiple single-antenna information users (IUs) and energy users (EUs), respectively. We aim to minimize the transmit power at the AP via jointly optimizing its transmit precoders and the reflect phase shifts at all IRSs, subject to the quality-of-service (QoS) constraints at all users, namely, the individual signal-to-interference-plus-noise ratio (SINR) constraints at IUs and the energy harvesting constraints at EUs. However, this optimization problem is non-convex with intricately coupled variables, for which the existing alternating optimization approach is shown to be inefficient as the number of QoS constraints increases. To tackle this challenge, we first apply proper transformations on the QoS constraints and then propose an efficient iterative algorithm by applying the penalty-based optimization method. Moreover, by exploiting the short-range coverage of IRS, we further propose a more computationally efficient algorithm by optimizing the phase shifts at all IRSs in parallel. Simulation results demonstrate the effectiveness of employing multiple IRSs for enhancing the performance of SWIPT systems as well as the significant performance gains achieved by our proposed algorithms over benchmark schemes. The impact of IRS on the transmitter/receiver design for SWIPT is also unveiled.

This paper investigates unmanned aerial vehicle (UAV)-aided backscatter communication (BackCom) networks, where the UAV is leveraged to help the backscatter device (BD) forward signals to the receiver. Based on the presence or absence of a direct link between BD and receiver, two protocols, namely transmit-backscatter (TB) protocol and transmit-backscatter-relay (TBR) protocol, are proposed to utilize the UAV to assist the BD. In particular, we formulate the system throughput maximization problems for the two protocols by jointly optimizing the time allocation, reflection coefficient and UAV trajectory. Different static/dynamic circuit power consumption models for the two protocols are analyzed. The resulting optimization problems are shown to be non-convex, which are challenging to solve. We first consider the dynamic circuit power consumption model, and decompose the original problems into three sub-problems, namely time allocation optimization with fixed UAV trajectory and reflection coefficient, reflection coefficient optimization with fixed UAV trajectory and time allocation, and UAV trajectory optimization with fixed reflection coefficient and time allocation. Then, an efficient iterative algorithm is proposed for both protocols by leveraging the block coordinate descent method and successive convex approximation (SCA) techniques. In addition, for the static circuit power consumption model, we obtain the optimal time allocation with a given reflection coefficient and UAV trajectory and the optimal reflection coefficient with low computational complexity by using the Lagrangian dual method. Simulation results show that the proposed protocols are able to achieve significant throughput gains over the compared benchmarks.

In this paper, we investigate the unmanned aerial vehicle (UAV)-aided simultaneous uplink and downlink transmission networks, where one UAV acting as a disseminator is connected to multiple access points (AP), and the other UAV acting as a base station (BS) collects data from numerous sensor nodes (SNs). The goal of this paper is to maximize the system throughput by jointly optimizing the 3D UAV trajectory, communication scheduling, and UAV-AP/SN transmit power. We first consider a special case where the UAV-BS and UAV-AP trajectories are pre-determined. Although the resulting problem is an integer and non-convex optimization problem, a globally optimal solution is obtained by applying the polyblock outer approximation (POA) method based on the problem's hidden monotonic structure. Subsequently, for the general case considering the 3D UAV trajectory optimization, an efficient iterative algorithm is proposed to alternately optimize the divided sub-problems based on the successive convex approximation (SCA) technique. Numerical results demonstrate that the proposed design is able to achieve significant system throughput gain over the benchmarks. In addition, the SCA-based method can achieve nearly the same performance as the POA-based method with much lower computational complexity.

Intelligent reflecting surface (IRS) is a cost-effective solution for achieving high spectrum and energy efficiency in future wireless networks by leveraging massive low-cost passive elements that are able to reflect the signals with adjustable phase shifts. Prior works on IRS mainly consider continuous phase shifts at reflecting elements, which are practically difficult to implement due to the hardware limitation. In contrast, we study in this paper an IRS-aided wireless network, where an IRS with only a finite number of phase shifts at each element is deployed to assist in the communication from a multi-antenna access point (AP) to multiple single-antenna users. We aim to minimize the transmit power at the AP by jointly optimizing the continuous transmit precoding at the AP and the discrete reflect phase shifts at the IRS, subject to a given set of minimum signal-to-interference-plus-noise ratio (SINR) constraints at the user receivers. The considered problem is shown to be a mixed-integer non-linear program (MINLP) and thus is difficult to solve in general. To tackle this problem, we first study the single-user case with one user assisted by the IRS and propose both optimal and suboptimal algorithms for solving it. Besides, we analytically show that as compared to the ideal case with continuous phase shifts, the IRS with discrete phase shifts achieves the same squared power gain in terms of asymptotically large number of reflecting elements, while a constant proportional power loss is incurred that depends only on the number of phase-shift levels. The proposed designs for the single-user case are also extended to the general setup with multiple users among which some are aided by the IRS. Simulation results verify our performance analysis as well as the effectiveness of our proposed designs as compared to various benchmark schemes.

Intelligent reflecting surface (IRS) is a cost-effective solution for achieving high spectrum and energy efficiency in future wireless communication systems by leveraging massive low-cost passive elements that are able to reflect the signals with adjustable phase shifts. Prior works on IRS mostly consider continuous phase shifts at each reflecting element, which however, is practically difficult to realize due to the hardware limitation. In contrast, we study in this paper an IRS-aided wireless network, where an IRS with only a finite number of phase shifts at each element is deployed to assist in the communication from a multi-antenna access point (AP) to a single-antenna user. We aim to minimize the transmit power at the AP by jointly optimizing the continuous transmit beamforming at the AP and discrete reflect beamforming at the IRS, subject to a given signal-to-noise ratio (SNR) constraint at the user receiver. We first propose a suboptimal and low-complexity solution to the problem by applying the alternating optimization technique. Then, we analytically show that as compared to the ideal case with continuous phase shifts, the IRS with discrete phase shifts achieves the same squared power gain in terms of asymptotically large number of reflecting elements, while a constant performance loss is incurred that depends only on the number of phase-shift levels. Simulation results verify our analytical result as well as the effectiveness of our proposed design as compared to different benchmark schemes.

Secure wireless information and power transfer based on directional modulation is conceived for amplify-and-forward relaying networks. Explicitly, we first formulate a secrecy rate maximization (SRM) problem, which can be decomposed into a twin-level optimization problem and solved by a one-dimensional (1D) search and semidefinite relaxation (SDR) technique. Subsequently, in order to reduce the search complexity, we formulate an optimization problem based on maximizing the signal-to-leakage-AN-noise-ratio (Max-SLANR) criterion, and transform it into a SDR problem. In addition, the relaxation is proved to be tight according to the classic Karush-Kuhn-Tucker (KKT) conditions. Finally, to reduce the computational complexity, a successive convex approximation (SCA) scheme is proposed to find a near-optimal solution. The complexity of the SCA scheme is much lower than that of the SRM and the Max-SLANR schemes. Simulation results demonstrate that the performance of the SCA scheme is very close to that of the SRM scheme in terms of its secrecy rate and bit error rate, but much better than that of the zero forcing scheme.

Unmanned aerial vehicles (UAVs) have found numerous applications and are expected to bring fertile business opportunities in the next decade. Among various enabling technologies for UAVs, wireless communication is essential and has drawn significantly growing attention in recent years. Compared to the conventional terrestrial communications, UAVs' communications face new challenges due to their high altitude above the ground and great flexibility of movement in the 3-D space. Several critical issues arise, including the line-of-sight (LoS) dominant UAV-ground channels and induced strong aerial-terrestrial network interference, the distinct communication quality-of-service (QoS) requirements for UAV control messages versus payload data, the stringent constraints imposed by the size, weight, and power (SWAP) limitations of UAVs, as well as the exploitation of the new design degree of freedom (DoF) brought by the highly controllable 3-D UAV mobility. In this article, we give a tutorial overview of the recent advances in UAV communications to address the above issues, with an emphasis on how to integrate UAVs into the forthcoming fifth-generation (5G) and future cellular networks. In particular, we partition our discussion into two promising research and application frameworks of UAV communications, namely UAV-assisted wireless communications and cellular-connected UAVs, where UAVs are integrated into the network as new aerial communication platforms and users, respectively. Furthermore, we point out promising directions for future research.

The line-of-sight (LoS) air-to-ground channel brings both opportunities and challenges in cellular-connected unmanned aerial vehicle (UAV) communications. On one hand, the LoS channels make more cellular base stations (BSs) visible to a UAV as compared to the ground users, which leads to a higher macro-diversity gain for UAV-BS communications. On the other hand, they also render the UAV to impose/suffer more severe uplink/downlink interference to/from the BSs, thus requiring more sophisticated inter-cell interference coordination (ICIC) techniques with more BSs involved. In this paper, we consider the uplink transmission from a UAV to cellular BSs, under spectrum sharing with the existing ground users. To investigate the optimal ICIC design and air-ground performance trade-off, we maximize the weighted sum-rate of the UAV and existing ground users by jointly optimizing the UAV's uplink cell associations and power allocations over multiple resource blocks. However, this problem is non-convex and difficult to be solved optimally. We first propose a centralized ICIC design to obtain a locally optimal solution based on the successive convex approximation (SCA) method. As the centralized ICIC requires global information of the network and substantial information exchange among an excessively large number of BSs, we further propose a decentralized ICIC scheme of significantly lower complexity and signaling overhead for implementation, by dividing the cellular BSs into small-size clusters and exploiting the LoS macro-diversity for exchanging information between the UAV and cluster-head BSs only. Numerical results show that the proposed centralized and decentralized ICIC schemes both achieve a near-optimal performance, and draw important design insights based on practical system setups.

Intelligent reflecting surface (IRS) is a revolutionary and transformative technology for achieving spectrum and energy efficient wireless communication cost-effectively in the future. Specifically, an IRS consists of a large number of low-cost passive elements each being able to reflect the incident signal independently with an adjustable phase shift so as to collaboratively achieve three-dimensional (3D) passive beamforming without the need of any transmit radio-frequency (RF) chains. In this paper, we study an IRS-aided single-cell wireless system where one IRS is deployed to assist in the communications between a multi-antenna access point (AP) and multiple single-antenna users. We formulate and solve new problems to minimize the total transmit power at the AP by jointly optimizing the transmit beamforming by active antenna array at the AP and reflect beamforming by passive phase shifters at the IRS, subject to users' individual signal-to-interference-plus-noise ratio (SINR) constraints. Moreover, we analyze the asymptotic performance of IRS's passive beamforming with infinitely large number of reflecting elements and compare it to that of the traditional active beamforming/relaying. Simulation results demonstrate that an IRS-aided MIMO system can achieve the same rate performance as a benchmark massive MIMO system without using IRS, but with significantly reduced active antennas/RF chains. We also draw useful insights into optimally deploying IRS in future wireless systems.

Unmanned aerial vehicle (UAV) communication is anticipated to be widely applied in the forthcoming fifth-generation wireless networks, due to its many advantages such as low cost, high mobility, and on-demand deployment. However, the broadcast and line-of-sight nature of air-to-ground wireless channels give rise to a new challenge on how to realize secure UAV communications with the destined nodes on the ground. This paper aims to tackle this challenge by applying the physical layer security technique. We consider both the downlink and uplink UAV communications with a ground node, namely, UAV-to-ground (U2G) and ground-to-UAV (G2U) communications, respectively, subject to a potential eavesdropper on the ground. In contrast to the existing literature on the wireless physical layer security only with the ground nodes at fixed or quasi-static locations, we exploit the high mobility of the UAV to proactively establish favorable and degraded channels for the legitimate and eavesdropping links, through its trajectory design. We formulate new problems to maximize the average secrecy rates of the U2G and G2U transmissions, by jointly optimizing the UAV's trajectory, and the transmit power of the legitimate transmitter over a given flight period of the UAV. Although the formulated problems are non-convex, we propose iterative algorithms to solve them efficiently by applying the block coordinate descent and successive convex optimization methods. Specifically, both the transmit power and UAV trajectory are optimized, with the other being fixed in an alternating manner, until the algorithms converge. The simulation results show that the proposed algorithms can improve the secrecy rates for both U2G and G2U communications, as compared to other benchmark schemes without power control and/or trajectory optimization.

The peculiar line-of-sight (LoS) propagation in air-to-ground channel provides both opportunities and challenges for the emerging cellular-connected unmanned aerial vehicle (UAV) communications. On one hand, the LoS channels make more cellular base stations (BSs) visible to a UAV as compared to ground users, which leads to a higher macro-diversity gain as compared to the ground users. On the other hand, the LoS channels also render the UAV to generate/receive more severe uplink/downlink interference to/from the BSs, thus requiring more sophisticated inter-cell interference coordination (ICIC) techniques with more BSs involved. To draw essential insight, this paper studies the uplink transmission from a UAV to cellular BSs. To mitigate the UAV's interference effect, we aim to maximize the sum-rate of the UAV and all ground users in its resulted ICIC region by jointly optimizing the UAV's cell association, resource block (RB) allocation, and transmit power. We first propose a centralized ICIC design that achieves the optimal performance. As the centralized ICIC requires global information of the network and substantial information exchange among an excessively large number of BSs, we further propose a decentralized ICIC scheme of significantly lower complexity and overhead for implementation. Specifically, we divide the cellular BSs into clusters, each with a dedicated cluster head for collecting information from its cluster BSs and exchanging information with the UAV by exploiting the LoS-induced macro-diversity. Numerical results show that the proposed decentralized ICIC scheme achieves a performance close to the optimal centralized design, and also outperforms the traditional ICIC scheme for cellular networks with terrestrial interference only.

Intelligent reflecting surface (IRS) is envisioned to have abundant applications in future wireless networks by smartly reconfiguring the signal propagation for performance enhancement. Specifically, an IRS consists of a large number of low-cost passive elements each reflecting the incident signal with a certain phase shift to collaboratively achieve beamforming and suppress interference at one or more designated receivers. In this paper, we study an IRS-enhanced point-to-point multiple-input single-output (MISO) wireless system where one IRS is deployed to assist in the communication from a multi-antenna access point (AP) to a single-antenna user. As a result, the user simultaneously receives the signal sent directly from the AP as well as that reflected by the IRS. We aim to maximize the total received signal power at the user by jointly optimizing the (active) transmit beamforming at the AP and (passive) reflect beamforming by the phase shifters at the IRS. We first propose a centralized algorithm based on the technique of semidefinite relaxation (SDR) by assuming the global channel state information (CSI) available at the IRS. Since the centralized implementation requires excessive channel estimation and signal exchange overheads, we further propose a low-complexity distributed algorithm where the AP and IRS independently adjust the transmit beamforming and the phase shifts in an alternating manner until the convergence is reached. Simulation results show that significant performance gains can be achieved by the proposed algorithms as compared to benchmark schemes. Moreover, it is verified that the IRS is able to drastically enhance the link quality and/or coverage over the conventional setup without the IRS.

Unmanned aerial vehicles (UAVs) have recently gained growing popularity in wireless communications owing to their many advantages such as swift and cost-effective deployment, line-of-sight (LoS) aerial-to-ground link, and controllable mobility in three-dimensional (3D) space. Although prior works have exploited the UAV's mobility to enhance the wireless communication performance under different setups, the fundamental capacity limits of UAV-enabled/aided multiuser communication systems have not yet been characterized. To fill this gap, we consider, in this paper, a UAV-enabled two-user broadcast channel (BC), where a UAV flying at a constant altitude is deployed to send independent information to two users at different fixed locations on the ground. We aim to characterize the capacity region of this new type of BC over a given UAV flight duration, by jointly optimizing the UAV's trajectory and transmit power/rate allocations over time, subject to the UAV's maximum speed and maximum transmit power constraints. First, to draw essential insights, we consider two special cases with asymptotically large/low UAV flight duration/speed, respectively. For the former case, it is shown that a simple hover-fly-hover (HFH) UAV trajectory with time division multiple access (TDMA)-based orthogonal multiuser transmission is capacity-achieving; while in the latter case, the UAV should hover at a fixed location that is nearer to the user with larger achievable rate and in general superposition coding (SC)-based non-orthogonal transmission with interference cancellation at the receiver of the nearer user is required. Next, we consider the general case with finite UAV speed and flight duration. We show that the optimal UAV trajectory should follow a general HFH structure, i.e., the UAV successively hovers at a pair of optimal initial and final locations above the line segment connecting the two users each with a certain amount of time and flies unidirectionally between them at the maximum speed, and SC is generally needed. Furthermore, when TDMA-based transmission is considered for low-complexity implementation, we show that the optimal UAV trajectory still follows an HFH structure, but the hovering locations can only be those above the two users. Extensive simulation results are provided to verify our analysis, which also reveal useful guidelines to the practical design of UAV trajectory and communication jointly.

The use of unmanned aerial vehicles (UAVs) as communication platforms is of great significance in future wireless networks, especially for on-demand deployment in temporary events and emergency situations. Although prior works have shown the performance improvement by exploiting the UAV's mobility, they mainly focus on delay-tolerant applications. As delay requirements fundamentally limit the UAV's mobility, it remains unknown whether the UAV is able to provide any performance gain in delay-constrained communication scenarios. Motivated by the above, we study, in this paper, an UAV-enabled orthogonal frequency-division multiple access (OFDMA) network where an UAV is dispatched as the mobile base station (BS) to serve a group of users on the ground. We consider a minimum-rate ratio (MRR) for each user, defined as the minimum instantaneous rate required over the average achievable throughput, to flexibly adjust the percentage of its delay-constrained data traffic. Under a given set of constraints on the users' MRRs, we aim to maximize the minimum average throughput of all users by jointly optimizing the UAV trajectory and OFDMA resource allocation. First, we show that the max-min throughput in general decreases as the users' MRRs become larger, which reveals a fundamental throughput-delay tradeoff in UAV-enabled communications. Next, we propose an iterative parameter-assisted block coordinate descent method to optimize the UAV trajectory and OFDMA resource allocation alternately, by applying the successive convex optimization and the Lagrange duality, respectively. Furthermore, an efficient and systematic UAV trajectory initialization scheme is proposed based on the simple circular trajectory. Finally, simulation results are provided to verify our theoretical findings and demonstrate the effectiveness of our proposed designs.

Due to the high maneuverability, flexible deployment, and low cost, unmanned aerial vehicles (UAVs) have attracted significant interest recently in assisting wireless communication. This paper considers a multi-UAV enabled wireless communication system, where multiple UAV-mounted aerial base stations are employed to serve a group of users on the ground. To achieve fair performance among users, we maximize the minimum throughput over all ground users in the downlink communication by optimizing the multiuser communication scheduling and association jointly with the UAV's trajectory and power control. The formulated problem is a mixed integer nonconvex optimization problem that is challenging to solve. As such, we propose an efficient iterative algorithm for solving it by applying the block coordinate descent and successive convex optimization techniques. Specifically, the user scheduling and association, UAV trajectory, and transmit power are alternately optimized in each iteration. In particular, for the nonconvex UAV trajectory and transmit power optimization problems, two approximate convex optimization problems are solved, respectively. We further show that the proposed algorithm is guaranteed to converge. To speed up the algorithm convergence and achieve good throughput, a low-complexity and systematic initialization scheme is also proposed for the UAV trajectory design based on the simple circular trajectory and the circle packing scheme. Extensive simulation results are provided to demonstrate the significant throughput gains of the proposed design as compared to other benchmark schemes.

The non-orthogonal multiple access (NOMA) technique can provide higher spectral efficiency and massive connectivities to wireless networks. The wireless power transfer (WPT) technique is a controllable and promising way to solve the energy scarcity problem of wireless devices. In this paper, we introduce the NOMA and WPT techniques into a multi-user wireless network, where multiple users need to transmit their information to an information receiver within a very limited spectrum and they suffer from the energy scarcity problem. We consider a harvest-then-transmit protocol by dividing each transmission block into two time-slots. In the first time-slot, a power station sends dedicated energy to the users via wireless energy beamforming. In the second time-slot, using their harvested energy in the previous time-slot, the users transmit their information to the information receiver in the NOMA manner. In this network, the throughput can be optimized by jointly optimizing the energy beamforming at the power station, the transmit powers of the users, as well as the time allocation between the two time-slots. We propose an algorithm to find the optimal solution of the throughput maximizing joint energy beamforming and resource allocation problem. Simulation results show that the proposed algorithm achieves higher throughput than the benchmark scheme.

The use of unmanned aerial vehicles (UAVs) as aerial base stations (BSs) is of great practical significance in future wireless networks, especially for on-demand deployment during a temporary event and emergency situation. Although prior works have demonstrated the performance improvement brought by the UAV mobility, they mainly focus on the delay-tolerant applications such as file transfer and data collection. As such, it is unknown if the UAV mobility is able to provide performance gain for delay-constrained applications, such as video conferencing and online gaming. Motivated by this, we study in this paper a UAV-enabled downlink orthogonal division multiple access (OFDMA) network where a UAV is dispatched to serve two ground users within a given flight period. By taking into account the delay-specified minimum-rate-ratio constraints of the users, our goal is to maximize the minimum user throughput by jointly optimizing the UAV trajectory and communication resource allocation. We show that the max-min user throughput in general decreases as the minimum-rate-ratio constraints become more stringent, which reveals a fundamental tradeoff between the throughput gain by exploiting the UAV mobility and the user delay requirement. Simulation results verify our theoretical findings and also demonstrate the effectiveness of our proposed design.

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

Unmanned aerial vehicles (UAVs) have attracted significant interest recently in wireless communication due to their high maneuverability, flexible deployment, and low cost. This paper studies a UAV-enabled wireless network where the UAV is employed as an aerial mobile base station (BS) to serve a group of users on the ground. To achieve fair performance among users, we maximize the minimum throughput over all ground users by jointly optimizing the multiuser communication scheduling and UAV trajectory over a finite horizon. The formulated problem is shown to be a mixed integer non-convex optimization problem that is difficult to solve in general. We thus propose an efficient iterative algorithm by applying the block coordinate descent and successive convex optimization techniques, which is guaranteed to converge. To achieve fast convergence and stable throughput, we further propose a low-complexity initialization scheme for the UAV trajectory design based on the simple circular trajectory. Extensive simulation results are provided which show significant throughput gains of the proposed design as compared to other benchmark schemes.

Unmanned aerial vehicle (UAV) communications has drawn significant interest recently due to many advantages such as low cost, high mobility, and on-demand deployment. This paper addresses the issue of physical-layer security in a UAV communication system, where a UAV sends confidential information to a legitimate receiver in the presence of a potential eavesdropper which are both on the ground. We aim to maximize the secrecy rate of the system by jointly optimizing the UAV's trajectory and transmit power over a finite horizon. In contrast to the existing literature on wireless security with static nodes, we exploit the mobility of the UAV in this paper to enhance the secrecy rate via a new trajectory design. Although the formulated problem is non-convex and challenging to solve, we propose an iterative algorithm to solve the problem efficiently, based on the block coordinate descent and successive convex optimization methods. Specifically, the UAV's transmit power and trajectory are each optimized with the other fixed in an alternating manner until convergence. Numerical results show that the proposed algorithm significantly improves the secrecy rate of the UAV communication system, as compared to benchmark schemes without transmit power control or trajectory optimization.

Heterogeneous network is a novel network architecture proposed in long-term-evolution, which highly increases the capacity and coverage compared with the conventional networks. However, in order to provide the best services, appropriate resource management must be applied. In this paper, we consider the joint optimization problem of user association, subchannel allocation, and power allocation for downlink transmission in multi-cell multi-association orthogonal frequency division multiple access heterogeneous networks. To solve the optimization problem, we first divide it into two subproblems: 1) user association and subchannel allocation for fixed power allocation and 2) power allocation for fixed user association and subchannel allocation. Subsequently, we obtain a locally optimal solution for the joint optimization problem by solving these two subproblems alternately. For the first subproblem, we derive the globally optimal solution based on graph theory. For the second subproblem, we obtain a Karush-Kuhn-Tucker optimal solution by a low complexity algorithm based on the difference of two convex functions approximation method. In addition, the multi-antenna receiver case and the proportional fairness case are also discussed. Simulation results demonstrate that the proposed algorithms can significantly enhance the overall network throughput.

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

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

In this paper, we investigate spectrum-power trading between a small cell (SC) and a macro cell (MC), where the SC consumes power to serve the MC users (MUs) in exchange for some bandwidth from the MC. Our goal is to maximize the system energy efficiency (EE) of the SC while guaranteeing the quality of service of each MU as well as SC users (SUs). Specifically, given the minimum data rate requirement and the bandwidth provided by the MC, the SC jointly optimizes MU selection, bandwidth allocation, and power allocation while guaranteeing its own minimum required system data rate. The problem is challenging due to the binary MU selection variables and the fractional-form objective function. We first show that the bandwidth of an MU is shared with at most one SU in the SC. Then, for a given MU selection, the optimal bandwidth and power allocation are obtained by exploiting the fractional programming. To perform MU selection, we first introduce the concept of the trading EE to characterize the data rate obtained as well as the power consumed for serving an MU. We then reveal a sufficient and necessary condition for serving an MU without considering the total power constraint and the minimum data rate constraint: the trading EE of the MU should be higher than the system EE of the SC. Based on this insight, we propose a low complexity MU selection method and also investigate the optimality condition. Simulation results verify our theoretical findings and demonstrate that the proposed resource allocation achieves near-optimal performance.

In this paper, we consider wireless powered communication networks (WPCNs) where multiple users harvest energy from a dedicated power station and then communicate with an information receiving station in a time-division manner. Thereby, our goal is to maximize the weighted sum of the user energy efficiencies (WSUEEs). In contrast to the existing system-centric approaches, the choice of the weights provides flexibility for balancing the individual user EEs via joint time allocation and power control. We first investigate the WSUEE maximization problem without the quality of service constraints. Closed-form expressions for the WSUEE as well as the optimal time allocation and power control are derived. Based on this result, we characterize the EE tradeoff between the users in the WPCN. Subsequently, we study the WSUEE maximization problem in a generalized WPCN where each user is equipped with an initial amount of energy and also has a minimum throughput requirement. By exploiting the sum-of-ratios structure of the objective function, we transform the resulting non-convex optimization problem into a two-layer subtractive-form optimization problem, which leads to an efficient approach for obtaining the optimal solution. The simulation results verify our theoretical findings and demonstrate the effectiveness of the proposed approach.

Most of the existing works on energy-efficient wireless communications only consider the transmitter (Tx) or the receiver (Rx) side power consumption, but not both. Moreover, the circuit power consumption is often assumed to be constant regardless of the transmission rate or the bandwidth. In this paper, we investigate the system-level energy-efficient transmission in multi-radio access networks by considering joint Tx and Rx power consumption and adopting link-dependent dynamic circuit power model. A combinatorial-type optimization problem for user scheduling, radio-link activation, and power control is formulated with the objective of maximizing joint Tx and Rx energy efficiency (EE). We tackle this problem using a divide-and-conquer approach. Specifically, the concepts of link EE and user EE are first introduced, which have structures similar to the system EE. Then, we explore their hierarchical relationships and propose an optimal algorithm whose complexity is linear in the product of the total number of users and radio links. Furthermore, we investigate the EE maximization problem with minimum user data rate constraints. The divide-and-conquer approach is also applied to find a sub-optimal but efficient solution. Finally, comprehensive numerical results are provided to validate the theoretical findings and demonstrate the effectiveness of the proposed algorithms.

This paper considers a wireless powered communication network (WPCN), where multiple users harvest energy from a dedicated power station and then communicate with an information receiving station. Our goal is to investigate the maximum achievable energy efficiency (EE) of the network via joint time allocation and power control while taking into account the initial battery energy of each user. We first study the EE maximization problem in the WPCN without any system throughput requirement. We show that the EE maximization problem for the WPCN can be cast into EE maximization problems for two simplified networks via exploiting its special structure. For each problem, we derive the optimal solution and provide the corresponding physical interpretation, despite the nonconvexity of the problems. Subsequently, we study the EE maximization problem under a minimum system throughput constraint. Exploiting fractional programming theory, we transform the resulting nonconvex problem into a standard convex optimization problem. This allows us to characterize the optimal solution structure of joint time allocation and power control and to derive an efficient iterative algorithm for obtaining the optimal solution. Simulation results verify our theoretical findings and demonstrate the effectiveness of the proposed joint time and power optimization.

Most of the existing works on energy-efficient wireless communication systems only consider the transmitter (Tx) or the receiver (Rx) side power consumption but not both. Moreover, they often assume the static circuit power consumption. To be more practical, this paper considers the joint Tx and Rx power consumption in multiple-access radio networks, where the power model takes both the transmission power and the dynamic circuit power into account. We formulate the joint Tx and Rx energy efficiency (EE) maximization problem which is a combinatorial-type one due to the indicator function for scheduling users and activating radio links. The link EE and the user EE are then introduced which have the similar structure as the system EE. Their hierarchical relationships are exploited to tackle the problem using a divide-and-conquer approach, which is only of linear complexity. We further reveal that the static receiving power plays a critical role in the user scheduling. Finally, comprehensive numerical results are provided to validate our theoretical findings and demonstrate the effectiveness of the proposed algorithm for improving the system EE.

This paper considers wireless powered communication networks (WPCN). Our goal is to investigate the maximum network energy efficiency (EE) by joint time allocation and power control while taking account the initial battery energy level of each user. It is shown that the EE maximization problem for the WPCN can be cast into the EE maximization problems for two independent networks, i.e., purely wireless powered communication networks (PWPCN) or initial energy limited communication networks (IELCN). For the PWPCN, we find that: 1) in the wireless energy transfer (WET) stage, the power station always transmits with its maximum power; 2) it is not necessary for all users to transmit signals in the wireless information transmission (WIT) stage, but all scheduled users will deplete all of their energy; 3) the maximum system EE can always be achieved by exhausting all the available time. Based on these observations, we derive a closed-form expression for the system EE based on the user EE, which transforms the original problem into a user scheduling problem that can be solved efficiently. While for the IELCN, we reveal that the most energy-efficient transmission strategy is to only schedule the user who has the highest user EE. Simulation results validate our theoretical findings and demonstrate the effectiveness of the proposed scheme.

This paper investigates the joint transmitter and receiver optimization for the energy efficiency (EE) in orthogonal frequency-division multiple-access (OFDMA) systems. We first establish a holistic power dissipation model for OFDMA systems, including the transmission power, signal processing power, and circuit power from both the transmitter and receiver sides, while existing works only consider the one side power consumption and also fail to capture the impact of subcarriers and users on the system EE. The EE maximization problem is formulated as a combinatorial fractional problem that is NP-hard. To make it tractable, we transform the problem of fractional form into a subtractive-form one by using the Dinkelbach transformation and then propose a joint optimization method, which leads to the asymptotically optimal solution. To reduce the computational complexity, we decompose the joint optimization into two consecutive steps, where the key idea lies in exploring the inherent fractional structure of the introduced individual EE and the system EE. In addition, we provide a sufficient condition under which our proposed two-step method is optimal. Numerical results demonstrate the effectiveness of proposed methods, and the effect of imperfect channel state information is also characterized.

In this paper, we investigate the resource allocation for joint transmitter and receiver energy efficiency maximization in orthogonal frequency division multiple access (OFDMA) systems. An elaborate power dissipation model is proposed for OFDMA systems considering the transmission power from the base station side, the signal processing power and radio frequency (RF) circuit power from both sides. Then we formulate the energy efficiency maximization problem and propose a two-step method based on the relationship analysis of the single subcarrier-user (SU) pair energy efficiency and system energy efficiency. Specifically, we first pair each subcarrier with the user resulting in highest SU pair energy efficiency, which is motivated by a special case study. Then, we propose a linear complexity scheme by exploring the inherent fractional structure of the system energy efficiency, which is proved to be optimal for the power allocation with given SU pairing in the first step. Finally, we provide a sufficient condition under which our proposed two-step method is globally optimal. Numerical results demonstrate the effectiveness of the proposed method and we also find that exploiting more user diversity is not always beneficial from the perspective of energy efficiency.

This paper investigates the optimal energy-efficient transmission policy of multi-channels in energy harvesting systems. We configure the transmitter with the active mode in which the energy cost includes the basic operation cost and transmission cost and signal processing cost, while with the sleep mode only counting the basic operation cost. Then the energy efficiency maximization problem of joint transmission time and power allocation is formulated and studied in the offline manner. Based on the fractional optimization theory, we transform the original fractional optimization problem into a series of subtractive-form optimization problems which are then further transformed into convex optimization problems. Then characteristics of the optimal solution are described based on the analysis of transmission time and power allocation. Finally, a special case without considering the basic operation cost as previous works assumed is studied. We find that the optimal policy results a best subchannel scheduling which can be viewed as the peaky transmission. Through this, the energy cost of the sleep mode in previous case can be interpreted as the switching operation cost.