ABSTRACT This paper studies the energy efficiency (EE) maximisation problem in an intelligent reflecting surface (IRS)-assisted rate-splitting multiple access (RSMA)-aided non-orthogonal multiple access (NOMA) downlink MIMO system. A multi-antenna base station (BS) serves multiple single-antenna users with the assistance of an IRS composed of passive reflecting elements. The resulting EE maximisation problem is highly non-convex due to its fractional objective function, coupled transmit precoding and IRS phase-shift design, and the unit-modulus constraints of the IRS elements. To tackle this challenge, an alternating optimisation (AO) framework is developed, where the BS precoders and IRS phase shifts are iteratively optimised. Specifically, the IRS phase optimisation subproblem is reformulated using semidefinite programming (SDP), which enables an efficient and tractable solution. Numerical results demonstrate that the proposed IRS-assisted RSMA-aided NOMA scheme significantly outperforms conventional OMA and NOMA schemes in terms of EE. Moreover, the proposed design achieves consistent EE gains under various system settings, including different transmit SNRs, numbers of IRS elements, circuit power consumption levels, and user densities. These results highlight the effectiveness of combining RSMA with IRS-assisted passive beamforming for energy-efficient future wireless networks.

Wireless communication is vulnerable to deep fading and eavesdropping, especially in long-distance double-hop scenarios. To address these issues, this paper proposes the movable antenna (MA-assisted secure transmission scheme. This scheme is designed for a cooperative wireless communication system in the presence of multiple single-antenna eavesdroppers. Our objective is to maximize the achievable secrecy rate, by jointly optimizing the transmit beamforming and antenna positions vectors. To solve the highly non-convex problem due to the strongly coupled variables, the alternating optimization and projected gradient ascent (PGA) methods are designed to the joint update of MA position and beamforming. Furthermore, an artificial noise (AN) interference signal is introduced. Specifically, the legitimate link actively transmits jamming noise to eavesdroppers during signal transmission, thereby enhancing the channel capacity of the secure communication system. Finally, the simulation results show that the proposed MA-assisted secure transmission for the cooperative wireless communication system significantly outperforms conventional fixed-position antenna (FPA)-assisted systems by up to 70% and existing benchmarks in terms of secrecy rate.

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.

Integrated sensing and communication (ISAC) is an essential technique for overcoming spectrum congestion. However, the performance of traditional ISAC systems with fixed-position-antennas (FPA) is limited due to insufficient spatial degree of freedom (DoF) exploration. Recently, fluid antenna (FA) with reconfigurable antenna position is developed to enhance the sensing and communication performance by reshaping the channel. This paper investigates an FA-enhanced ISAC system where a base station is equipped with multiple FAs to communicate with multiple single-antenna users and with FPAs to sense a point target. In this paper, we consider both perfect and imperfect channel state information (CSI) of the communication channel and sensing channel. In two cases, we focus on the maximization of the sensing signal-to-noise (SNR) by optimizing the positions of FAs and the dual-functional beamforming under the constraints of the FA moving region, the minimum FA distance and the minimum signal-to-interference-plus-noise (SINR) per user. Specifically, for the ideal case of perfect CSI, an iterative alternating optimization (AO) algorithm is proposed to tackle the formulated problem where the dual-functional beamforming and the FA positions are obtained via semidefinite relaxation (SDR) and successive convex approximation (SCA) techniques. Then, for the imperfect CSI case, we propose an AO-based iterative algorithm where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {S}-$</tex-math></inline-formula>Procedure and SCA are applied to obtain the dual-functional beamforming and the FA positions. Furthermore, we analytically and numerically prove the convergence of the proposed algorithms. Numerical results demonstrate the notable gains and effectiveness of the proposed algorithms over baseline schemes.

This paper presents a novel model within a nonorthogonal multiple access (NOMA) system comprising a multiantenna access point (AP) and multiple single-antenna backscatter devices (BDs) and user equipments (UEs), where the channels from AP to UEs and BDs to UEs are characterized by imperfections. The core objective of this study is to optimize energy efficiency (EE) while ensuring the quality of service (QoS) for both uplink and downlink users. To achieve this, the paper employs the Dinkelbach method to address the fractional programming challenge, leverages the S-procedure and Bernstein-type inequality (BTI) for managing channel uncertainties, utilizes semidefinite relaxation (SDR) to tackle the nonconvexity arising from the quadratic form of the beamforming matrix, and applies sequential rank-one constrained relaxation (SROCR) to circumvent the computational complexity introduced by Gaussian randomization. Additionally, for the specific case of resource allocation with a single BD, a closed-form expression is derived. The simulation results demonstrate that the proposed algorithm outperforms the non-robust scheme in terms of EE, significantly enhancing the system's stability and robustness.

This paper proposes a novel dual-functional radar-communication (DFRC) framework that integrates unmanned aerial vehicle (UAV) communications into an integrated sensing and communication (ISAC) system, termed the ISAC-UAV architecture. In this system, the UAV’s mobility is leveraged to simultaneously serve multiple single-antenna uplink users (UEs) and perform radar-based sensing tasks. A key challenge stems from the target position uncertainty due to movement, which impairs matched filtering and beamforming, thereby degrading both uplink reception and sensing performance. Moreover, UAV energy consumption associated with mobility must be considered to ensure energy-efficient operation. We aim to jointly maximize radar sensing accuracy and minimize UAV movement energy over multiple time steps, while maintaining reliable uplink communications. To address this multi-objective optimization, we propose a deep reinforcement learning (DRL) framework based on a long short-term memory (LSTM)-enhanced deep deterministic policy gradient (DDPG) network. By leveraging historical target trajectory data, the model improves prediction of target positions, enhancing sensing accuracy. The proposed DRL-based approach enables joint optimization of UAV trajectory and uplink power control over time. Extensive simulations validate that our method significantly improves communication quality and sensing performance, while ensuring energy-efficient UAV operation. Comparative results further confirm the model’s adaptability and robustness in dynamic environments, outperforming existing UAV trajectory planning and resource allocation benchmarks.

This paper proposes a framework integrating Rate Splitting Multiple Access (RSMA), semantic communications, and generative AI for optimizing next-generation wireless networks. We present a unified model that combines RSMA with semantic communications to enhance both spectral efficiency and semantic fidelity. Our system model focuses on a downlink semantic communication system with a multi-antenna base station serving multiple single-antenna users. A dynamic contract-based incentive mechanism is developed to address user heterogeneity and information asymmetry in semantic RSMA scenarios. We introduce a diffusion model-based approach for joint optimization of resource allocation and contract design in RSMA systems. The semantic encoding process extracts key information, i.e., free-space detection, interest points, object attributes, and spatial relationships, from image data. A loss function is designed to train the semantic encoder and RSMA scheme, incorporating semantic extraction, partitioning, reconstruction, and task-specific components. Our framework includes a multi-objective performance evaluation that considers both conventional metrics and semantic accuracy in a multi-user RSMA environment. We also define a multi-component semantic accuracy metric to assess the quality and utility of the extracted semantic information. Extensive simulation results demonstrate the superiority of our proposed framework over existing approaches in terms of system throughput, energy efficiency, and semantic fidelity across various network scenarios and user distributions.

This paper investigates a multiuser scheduling problem within an uplink multiple-input multi-output (MIMO) status update network, consisting of a multi-antenna base station (BS) and multiple single-antenna devices. The presence of multiple antennas at the BS introduces spatial degrees-of-freedom, enabling concurrent transmission of status updates from multiple devices in each time slot. Our objective is to optimize network-wide information freshness, quantified by the age of information (AoI) metric, by determining how the BS can best schedule device transmissions, while taking into account the random arrival of status updates at the device side. To address this decision-making problem, we model it as a partially observable Markov decision process (POMDP) and establish that the evolution of belief states for different devices is independent. We also prove that feasible belief states can be described by finite-dimensional vectors. Building on these observations, we develop a dynamic scheduling (DS) policy that minimizes a configurable drift in each time slot to solve the POMDP, and then derive an upper bound of its AoI performance, which is used to optimize the parameter configuration. To gain more design insights, we investigate a symmetric network, and put forth a fixed scheduling (FS) policy that minimizes the drift with an optimized fixed number scheduling, thereby yielding lower computational complexity. An action space reduction strategy is applied to further reduce the computational complexity of both DS and FS policies. Our numerical results validate our analyses and indicate that the DS policy with the reduced action space performs almost identically to the original DS policy, and both outperform the baseline policies.

Flexible intelligent metasurface (FIM) technology shows promise in terms of enhancing both the spectral and energy efficiency of wireless networks. An FIM is composed of an array of low-cost radiating elements, each of which can independently radiate electromagnetic signals, while flexibly adjusting its position along the direction perpendicular to the surface by a process termed as “morphing”. This is of particular interest for wireless communication systems operating at millimeter-wave and terahertz frequencies, where deep fading generally occurs within a few millimeters. Hence, in contrast to conventional rigid 2D antenna arrays, the FIM surface shape may be reconfigured to improve the channel quality by beneficial 3D morphing. In this paper, we investigate the multiuser downlink, where an FIM deployed at a base station (BS) communicates with multiple single-antenna users. We formulate an optimization problem for minimizing the total downlink transmit power at the BS, by jointly optimizing the transmit beamforming and FIM surface shape, subject to an individual signal-to-interference-plus-noise ratio (SINR) constraint of each user as well as a constraint on the maximum FIM morphing range. To solve this problem, we first consider a simple single-user scenario and show that the optimal 3D surface shape is achieved by independently adjusting each FIM element to the position having the strongest channel gain. However, in realistic multiuser scenarios, FIM surface-shape morphing involves complex tradeoffs. To address this issue, an efficient alternating optimization method is proposed to iteratively update the FIM surface shape and the transmit beamformer to gradually reduce the transmit power. Additionally, we analyze the performance gain of the FIM, showcasing a logarithmic received power scaling law versus its maximum morphing range. Finally, simulation results show that the FIM reduces the transmit power by about 3 dB compared to conventional rigid 2D arrays at a given data rate. The code for this paper is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/JianchengAn</uri>.

Intelligent reflecting surfaces (IRSs) have emerged as a promising technology to enhance link reliability in a cost-effective manner, especially for line-of-sight (LOS) link blocking caused by obstacles. In this paper, we investigate an IRS-assisted single-cell photon-counting communication system in the presence of building shadows, where one IRS is deployed to assist the communication between a multi-antenna base station (BS) and multiple single-antenna users. Photon counting has been widely adopted in sixth-generation (6G) optical communications due to its exceptional detection capability for low-power optical signals. However, the correlation between signal and noise complicates analyses. To this end, we first derive the channel gain of the IRS-assisted MIMO system, followed by the derivation of the mean square error (MSE) of the system using probabilistic methods. Given the constraints of the transmit power and IRS configuration, we propose an optimization problem aimed at minimizing the MSE of the system. Next, we present an alternating optimization (AO) algorithm that transforms the original problem into two convex subproblems and analyze its convergence and complexity. Finally, numerical results demonstrate that the IRS-assisted scheme significantly reduces the MSE and bit error rate (BER) of the system, outperforming other baseline schemes.

This study delves into the energy optimization problem in Internet of Things (IoT) networks. We consider the downlink from multiple antenna Gateway (GW) and single antenna IoT devices. For this challenging nonconvex problem, we initially introduced the well-known zero-forcing beamforming (ZFBF) to eliminate inter-user interference, thereby transforming the energy efficiency maximization problem into a concave-convex fractional problem. Then, instead of applying a combination of ZFBF with power allocation, we propose the Particle Swarm Optimization (PSO) algorithm to allocate power to find the optimized beamforming matrix. Through extensive numerical analysis, we demonstrate the effectiveness of our proposed scheme in terms of energy efficiency and power achieved at the GW. The results underscore the significant benefits of our approach over conventional methods, paving the way for practical and efficient energy optimization in IoT networks.

We present a novel power-efficient and low-complexity scheme for cache-aided communication in networks with a multi-antenna base station that serves multiple single-antenna users. The scheme is based on transmitting coded messages to disjoint groups of users simultaneously and achieves an important trade-off between performance and complexity. The subpacketization level of the proposed scheme is sub-optimum compared to the state-of-the-art but is still feasible for a practical range of network parameters. On the other hand, the scheme achieves near-optimal performance and asymptotically achieves the same degrees of freedom (DoF) as the best-known schemes achieve. However, compared to other optimum achievable rates, the proposed scheme suffers from minor performance degradation due to power loss, which becomes negligible as the signal-to-noise ratio or the number of users grows. In return, the reductions in complexity and subpacketization allow for practical implementation of this scheme even for a large number of users. The presented scheme is also very flexible to the variation of the network topology and can easily be generalized to heterogeneous and dynamic scenarios.

This letter studies the performance of an uplink cell-free massive multiple-input multiple-output (MIMO) system with maximum-ratio combining receiver over Rayleigh fading channel, in which many distributed access points configured with multiple antennas provide services to multiple single-antenna users. For such a system, we derive an exact expression for probability density function (PDF) of individual signal-to-interference-plus-noise ratio (SINR). Due to its complexity for performance evaluation purposes, this PDF of SINR is approximated as a gamma distribution, and the resulting expression is used to obtain the approximate expressions of the coverage probability and the ergodic capacity, respectively. Besides, in order to ensure uniformly good coverage probability and ergodic capacity, a max-min power control algorithm is proposed, which can be formed by solving a geometric programming problem. Numerical results verify the correctness of the theoretical analysis and confirm the effectiveness of the proposed algorithm.

We investigate multiuser uplink communications from multiple single-antenna users to a base station (BS), which is equipped with multiple fluid antennas (FAs) and adopts zero-forcing receivers to decode multiple signals. We aim to optimize antennas’ positions at the BS, to minimize the total transmit power of all users subject to the minimum rate requirement. After applying transformations, we show that the problem is equivalent to minimizing the sum of each eigenvalue’s reciprocal of a matrix, which is a function of all antennas’ positions at the BS. Subsequently, the projected gradient descent (PGD) method is utilized to find a locally optimal solution. In particular, different from the latest related work, we exploit the eigenvalue decomposition to successfully derive a closed-form gradient for the PGD, which facilitates the practical implementation greatly. We demonstrate by simulations that via careful optimization for all antennas’ positions in our proposed design, the total transmit power of all users can be decreased significantly as compared to competitive benchmarks.

Intelligent Reflecting Surface (IRS) is a promising approach to effectively improve the propagation environment, which includes a controller and numerous reflecting elements. In this paper, we consider an IRS-assisted mobile edge computing (MEC) system, which has a base station (BS), multiple single-antenna user terminals (UTs), and an IRS. Aiming at minimizing the system energy consumption, the transmission power of UTs, the BS receiving beamforming vector, the BS computing resources allocation, and the IRS effective phase shifts are jointly optimized. As these four variables are coupled together and the problem is non-convex, block coordinate descent method is adopted to decompose the optimization problem into four subproblems. In order to address the transmission power subproblem, quadratic transform based fractional programming, Lagrange dual transformation, and difference of convex function algorithm are used. Quadratic transformation and Lagrange dual transformation are also used to optimize the phase shift matrix and the receiving beamforming vector, while the quadratic transform in the multidimensional and complex case is used additionally in the IRS phase-shift subproblem to tackle the fractional term. Meanwhile, the computation resource allocation is derived in a closed-form expression. Simulation results confirm that for the IRS-assisted MEC system, the proposed optimization method is effective.

Base station (BS) activation is a widely-used approach to alleviate the system power cost for device maintenance. However, frequently switching on/off BSs may also introduce extra power and signaling costs, which motivates us to limit the BS switching frequency when performing BS activation. To address this, we study a problem of joint long-term BS activation and short-term beamforming (J-LTBA-STBF) in a network where multiple multi-antenna BSs cooperatively serve multiple single-antenna users. LTBA means that each BS's active/inactive switching frequency is properly controlled to yield relatively stable BS-user association and limited switching power, while STBF lies in that due to the low cost of refreshing beamformers, the transmit beamformers are allowed to be updated frequently to maximally lower the transmit power. Following the idea, two efficient algorithms are designed, according to the data amount they request to start running, to optimize the active BSs and the BS transmit beamformers in multiple adjacent time slices. Consequently, the transmit, maintenance and switching powers can be well balanced to facilitate power-efficient communications. Numerical results validate the efficacies of the algorithms.

High-resolution fully digital transceivers are infeasible at millimeter-wave (mmWave) due to their increased power consumption, cost, and hardware complexity. The use of low-resolution converters is one possible solution to realize fully digital architectures at mmWave. In this paper, we consider a setting in which a fully digital base station with constant envelope quantized (CEQ) digital-to-analog converters on each radio frequency chain communicates with multiple single antenna users with individual signal-to-quantization-plus-interference-plus-noise ratio (SQINR) constraints over frequency selective channels. We first establish uplink downlink duality for the system with CEQ hardware constraints and OFDM-based transmission considered in this paper. Based on the uplink downlink duality principle, we present a solution to the multi-user multi-carrier beamforming and power allocation problem that maximizes the minimum SQINR over all users and sub-carriers. We then present a per sub-carrier version of the originally proposed solution that decouples all sub-carriers of the OFDM waveform resulting in smaller sub-problems that can be solved in a parallel manner. Our numerical results based on 3GPP channel models generated from Quadriga demonstrate improvements in terms of ergodic sum rate and ergodic minimum rate over state-of-the-art linear solutions. We also show improved performance over non-linear solutions in terms of the coded bit error rate with the increased flexibility of assigning individual user SQINRs built into the proposed framework.

In millimeter wave (mmWave) communication systems with massive multiple-input multiple-output (MIMO) architecture, selecting the antennas contributing most from the candidate array to transmit/receive signals is one of the effective solutions to reduce hardware cost and power consumption while maintaining high spectral efficiency. In this paper, for the communication systems where the base station (BS) equipped with massive MIMO antenna array communicates with multiple single-antenna users, the impact of limited-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) on system capacity is investigated, and two antenna selection (AS) algorithms, namely quantization-aware greedy with square maximum-volume (QAG-SMV) and group-selection (GS) schemes, are proposed to enhance system capacity for the uplink and downlink transmission, respectively. Specifically, after the quantization noise caused by limited-resolution ADCs/DACs is converted to independent additive noise, the problem of maximizing system capacity is formulated. Then, two novel AS schemes are proposed to improve system capacity. Simulation results show that the proposed AS algorithms can obtain higher average system capacity, and the computational complexity is reduced as well.

This study examines a downlink multiple-input single-output (MISO) system, where a base station (BS) with multiple antennas sends data to multiple single-antenna users with the help of a reconfigurable intelligent surface (RIS) and a half-duplex decode-and-forward (DF) relay. The system’s sum rate is maximized through joint optimization of active beamforming at the BS and DF relay and passive beamforming at the RIS. The conventional alternating optimization algorithm for handling this complex design problem is suboptimal and computationally intensive. To overcome these challenges, this letter proposes a two-phase graph neural network (GNN) model that learns the joint beamforming strategy by exchanging and updating relevant relational information embedded in the graph representation of the transmission system. The proposed method demonstrates superior performance compared to existing approaches, robustness against channel imperfections and variations, generalizability across varying user numbers, and notable complexity advantages.

In this paper, we explore the use of reconfigurable intelligent surface (RIS) in unmanned aerial vehicle (UAV) based multiuser downlink communications, where a flying UAV serves multiple single antenna users through multiple RISs mounted on various buildings. More specifically, we consider the selection of RISs based on the outdated and imperfect channel state information (CSI) of the composite UAV-RIS-User channels at the UAV. After selection process, the UAV communicates to the user via the selected RISs and also with the direct link. Particularly, we derive an infinite series based expression for selection probability of RISs under both the outdated and imperfect CSI of composite channels based selection scheme. We also derive the statistical distribution of instantaneously received signal-to-noise ratio (SNR) under outdated and imperfect CSI conditions of both the direct and composite links at the user. Next, using the derived statistics, we analyze the network’s performance in terms of the average coverage probability (ACP) and average bit error rate (ABER) over the complete UAV flight time. Moreover, we discuss the behavior of ACP and ABER for very small and very large values of UAV transmit power, respectively. It is depicted through numerical results that selecting more RISs from a group of small-sized RISs may not be as advantageous as selecting fewer RISs from a group of large-sized RISs. Moreover, we also demonstrate the effect of several system parameters such as number of RIS reflecting elements, number of selected RISs, the severity of UAV-RIS and RIS-User links, and the severity of imperfect and outdated CSI on the network’s performance. The analytical results are corroborated with Monte-Carlo simulations.