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.

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.

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.

In this paper, we investigate an IRS-assisted hybrid active and passive jamming communication method in single-user scenarios. This paper aims to minimizing the signal-to-interference-plus-noise ratio (SINR) of users by optimizing intelligent reflecting surface (IRS) phase shifts to disrupt the communication between users. Due to the nonconvexity of the formulated problem, semidefinite relaxation (SDR) and Charnes-Cooper transformation (CCT) methods are explored to solve the problem. The simulation results demonstrate that the hybrid optimization jamming method significantly outperforms the individual active or passive jamming methods.

Device-to-Device (D2D) content sharing supports real-time applications but still faces challenges of large data and limited resources. With the growing computing capabilities of terminal devices, semantic content sharing has emerged as a promising solution. In this paper, a cooperative transmission D2D semantic content sharing based on probabilistic graphs is investigated to enhance user quality of experience (QoE). Specifically, D2D is divided into requesters and helpers. It is noteworthy that if both matching entities have knowledge bases, smaller-sized semantic information can be obtained by further compressing the semantic data. To encourage cooperation between D2D, we design utility functions for helpers and requesters, and formulate an optimization problem to maximize system utility by optimizing compression ratio, transmit power, and D2D pairing. In order to solve this problem, we introduce a matching game framework. First, we use an Nelder-Mead (NM)-based heuristic algorithm to solve the optimization problem for the compression ratio and transmit power. Then, a distributed cooperative semantic content sharing matching algorithm is proposed to achieve one-to-one matching, ultimately resulting in a stable strategy. The simulation results validate the optimality and convergence of the proposed algorithm. Compared to classical distributed algorithms, the proposed algorithm improves QoE performance by over 4.8%.

The environmental shelters such as public links can enable covert communication by covering covert transmission. To further exploit shelters, this paper focuses on a two-hop system where multiple pairs of public users and one pair of covert users communicate through a multi-antenna relay. We aim to improve covertness performance while satisfying the covertness constraints of two hops and quality of service (QoS) requirements of public users. The covert throughput maximization problem is formulated via jointly optimizing transmit power and beamformer, which is challenging to solve. We introduce successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques to convert the problem into convex, where the joint optimization algorithm is developed. Considering the computational complexity, we further design a block diagonalization (BD) beamformer at the relay, which translates the beamformer optimization into a power allocation problem and derives the optimal solution in a closed form. We analytically show that the covert throughput first increases and then decreases as the number of public pairs increases in BD-based design, which has been verified numerically and can be generalized in other designs. Numerical results also evaluate the superiority of the joint optimization algorithm and the effectiveness of the BD-based efficient design. In particular, the BD-based design is very close to the joint optimization under small maximum transmit power of users or large maximum transmit power of relay.

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.

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.

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.

In this article, we investigate covert millimeter-wave (mmWave) communications with finite blocklength, where a multiantenna transmitter sends covert messages to a legitimate receiver in the presence of spatially random wardens. Both the phase array (PA) and linear frequency diverse array (LFDA) beamforming schemes, which are designed to maximize the antenna gain from the transmitter to the legitimate receiver, are investigated to improve the covert communication performance. First, the novel expressions of covert communication constraint and average effective covert throughput (AECT) are derived for both beamforming schemes. Then, taking into account the constraint of maximal available blocklength, the optimal transmit power and blocklength are determined for maximizing the AECT. Typically, comparing to the benchmark with fixed blocklength, the enhancement of AECT by utilizing the optimized blocklength enlarges as the density of wardens increases. In addition, it is observed that increasing the maximal available blocklength cannot always improve the maximum AECT due to the tradeoff between the transmit power and blocklength. Furthermore, it is shown that the maximum AECT varies for different directions of the legitimate receiver under both the beamforming schemes, and the transmitter can adaptively choose the PA or LFDA beamforming scheme to improve the covertness performance against spatially random wardens.

Intelligent reflective surfaces (IRSs) as low energy consumption and easy to attach devices have been widely applied in the field of anti-jamming recently. In particular, the combination of IRS and unmanned aerial vehicle (UAV), as IRS-UAV, further expands the scope of IRS services. In this article, the joint IRS selection and beamforming optimization problem has been investigated in multiple IRS-UAV-assisted anti-jamming D2D networks. To solve the above optimization problem, a distributed matching-based selection and$Q$-learning-based beamforming optimization algorithm (DMQ) was proposed. In detail, the optimization problem is decomposed into two subproblems, namely, the IRS selection subproblem is formulated as a noncommutative many-to-many matching game model to describe peer effects and uncertainty selection quotas, and the passive beamforming optimization subproblem is solved by a reinforcement algorithm to satisfy the complex environment. Numerical simulations confirm the convergence and near-optimal performance of the proposed scheme with lower latency and greater robustness.

Unmanned aerial vehicles (UAVs) can utilize multiaccess edge computing (MEC) to help Internet of Things (IoT) devices complete the complex status update by efficient offloading and proper trajectory design. However, considering that IoT devices usually communicate with UAVs in the finite blocklength regime, the uplink transmission cannot be error free. Due to the nonzero packet error probability (PEP), it is difficult to evaluate the instantaneous system performance as in the case with small blocklength. Moreover, the PEP is simultaneously coupled with the offloading parameters and trajectories of UAVs, which makes the performance optimization even more challenging. To this end, we first derive the analytical expressions of the average peak Age of Information (AoI), the average energy consumption of IoT devices and the average energy consumption of UAVs. Then, we formulate a joint optimization problem aiming to minimize the weighted sum of the three performance metrics by jointly optimizing the offloading parameters and the UAV trajectories. By dividing the original problem into multiple subproblems, an alternating optimization-based algorithm is proposed to solve it suboptimally. Simulation results validate the effectiveness of our proposed algorithm and reveal that by properly setting the transmit power and computing capacity of IoT devices, the desired tradeoff among the three performance metrics can be achieved and thus the system performance can be improved effectively.

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.

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.

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.

In this paper, the authors propose two cognitive relaying schemes based on overlay and underlay, which exploit the cooperation opportunities inherent in primary retransmission to improve secondary throughput. If a primary signal is not decoded by the primary receiver (PR), a secondary user (SU) can be selected to relay it invisibly along with the primary retransmission. For overlay cognitive relaying, SUs intend to reduce primary retransmission time by relaying primary message, so that more access opportunities are available. While in underlay cognitive relaying, SU allocates part of its power to help primary user (PU) and the remaining power is used to transmit secondary message simultaneously. By controlling the phase of the relay signal, signals retransmitted from primary transmitter (PT) and SU can constructively combine at the PR. In both relaying schemes, we consider the best-relay selection as well. We define some novel metrics to evaluate the performance of PU and SU. For PU, we study the improvements in outage performance and average transmitting time per packet, while for SU, we consider the cooperation gain and cooperation efficiency, respectively. Theoretical analysis and numerical results verify the validity of both schemes, and a comparison is made between them.

In this paper, we propose a hybrid cooperative scheme to improve the secrecy rate for a cooperative network in presence of multiple relays. Each relay node transmits a mixed signal consisting of weighted source signal and intentional noise. The problem of power allocation and joint design of beamforming and jamming weights are investigated, and an iterative solution for the secrecy rate maximization problem is presented. The numerical results demonstrate that the proposed hybrid scheme further improves secrecy rate, as compared to traditional cooperative schemes.