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.

The open and vulnerable nature of wireless channels exacerbates security risks in integrated sensing and communication (ISAC) systems, especially when the sensing targets act as potential eavesdroppers (Eves), and these risks intensify with collusion among Eves. To address this challenge, this paper investigates a novel strategy for a robust reconfigurable intelligent surfaces (RIS)-assisted secure ISAC system, where an ISAC base station facilitates simultaneous secure communication with legitimate users and sensing of multiple targets that may serve as Eves. We examine two different interaction mechanisms among Eves, namely, non-colluding Eves (NCE) and colluding Eves (CE), under both perfect and imperfect channel state information (CSI) assumptions. For both mechanisms, we formulate the optimization problem of maximizing users’ sum secrecy rate by jointly designing the transmit beamforming and RIS phase-shifts. This optimization is subject to constraints on transmit power, sensing requirements, and unit-modulus RIS phase shifts. The resulting non-convex problems are solved via alternating optimization (AO) algorithms. Specifically, in order to handle the severely non-convex and coupled objective function and multi-link accumulated channel error constraints caused by CE as well as imperfect CSI, we employ the majorizationminimization algorithm and the S-procedure to convert these problems into tractable forms. Simulation results validate the effectiveness of our proposed algorithms. We highlight that, at the expense of a 15% reduction in the users’ sum rate, our proposed algorithm achieves up to a 185% increase in the sum secrecy rate. Furthermore, we quantify the sensing-security trade-off by analyzing the reduction of the sum secrecy rate induced by sensing requirements, and we reveal the impacts of various factors on the sum secrecy rate, such as RIS element number, channel estimation errors, and sensing thresholds.

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.

Movable antenna (MA) technology enables flexible beamforming through adaptive adjustment of antenna positions, thereby enhancing communication performance. This paper investigates the MA-aided air-to-ground (A2G) system, where an unmanned aerial vehicle (UAV) equipped with MAs serves multiple ground users. The primary objective is to jointly optimize UAV deployment, antenna positions, beamforming, and power control to maximize the downlink communication sum rate. A key challenge lies in the difficulty of acquiring complete instantaneous channel state information (I-CSI), as the antennas cannot traverse all possible positions to obtain such full-scale I-CSI, which impedes the efficient optimization of system parameters. To address this challenge, we propose a two-stage transmission scheme: in the first stage, location-aware statistical CSI (S-CSI) is leveraged to optimize UAV deployment, power control, and MA positions for maximizing long-term system performance, while the second stage estimates I-CSI based on the optimized UAV and antenna positions to refine short-term transmit beamforming after long-term variables are determined, thereby substantially reducing CSI acquisition overhead. For long-term policy optimization, we introduce a novel dual-spatial-scale hierarchical deep reinforcement learning (DSSH-DRL) framework, which preserves flexibility in global policy formulation while enhancing training efficiency via effective decoupling of control variables. Simulation results demonstrate the effectiveness of the proposed algorithm and validate the promising application potential of MA technology in A2G communication networks.

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.

To address the challenge of anti-jamming transmission in wireless communication systems under complex interference environments, this paper proposes a movable antennas systems anti-jamming transmission scheme based on deep unfolding networks. Although existing research has enhanced the diversity of anti-jamming strategies through mobile antenna technology, its optimization algorithms are complex, system overhead is large, and traditional deep learning schemes face problems such as strong data dependence and poor interpretability. Therefore, this paper innovatively introduces deep unfolding networks into the design of movable antennas systems, achieving efficient optimization through the fusion of algorithm unfolding and neural networks. Specifically, for the multi-jammer confrontation scenario, the non-convex joint optimization problem is decoupled into an alternating optimization problem of the receiving beamforming vector and antenna position, where the beamforming is solved in closed form through the generalized Rayleigh quotient. Further, by mapping the iterative optimization steps to the hidden layers of the neural network, a lightweight deep unfolding framework is constructed, significantly reducing the computational complexity of traditional iterative algorithms. Simulation results verify the effectiveness and reliability of the proposed scheme in real-time anti-jamming transmission under jamming environments.

Due to the sharing of communication and sensing signals, integrated sensing and communication (ISAC) systems are vulnerable to potential eavesdropping attacks. This article investigates simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-enabled secure ISAC for Internet of Everything (IoE) networks. Two typical ISAC scenarios are explored: 1) communication enhancement (CE) and 2) communication-sensing fusion (CSF). In the CE scenario, the communication user (CU) is located in the Non-Line of Sight (NLoS) region of the ISAC base station (ISAC-BS), while the sensing target is located in the Line-of-Sight (LoS) region of the ISAC-BS. In the CSF scenario, both the CU and the sensing target are located in the NLoS region of the BS. The STAR-RIS is deployed to improve communication and sensing performance. The artificial noise (AN)-aided robust and secure beamforming problems are formulated for different scenarios based on imperfect channel knowledge. Several STAR-RIS protocols are also considered to provide a more complete system design and performance analysis. Efficient iteration-based algorithms are developed to solve these nonconvex and highly coupled problems. Based on simulation results, we observe that: 1) the proposed methods can effectively improve the system performance and 2) from the perspective of communication security, the dedicated AN signal is helpful for STAR-RIS-enabled ISAC systems.

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.

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.

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.

In this article, an intelligent reflecting surface (IRS)-assisted integrated satellite-unmanned aerial vehicle (UAV)-terrestrial (SUT) Internet of Things (IoT) network faced with a smart jammer under imperfect channel state information (CSI) conditions is considered. We propose a Stackelberg game model to describe the adversarial relationship between the satellite, UAV, and IRS and the jammer, which are modeled as the leader and the follower, respectively. For the follower subgame, the jammer aims to minimize the jamming power while guaranteeing that the jamming energy efficiency surpasses a certain threshold. Under this setup, the Angle of Arrival (AoA)-based discretization method is utilized to address the imperfect CSI issue. Then, the use-and-then-forget method and the Lagrangian function are employed to obtain a closed-form expression for the jammer’s power. Finally, a feasible jamming power solution is obtained by means of the Cauchy–Schwarz inequality. For the leader subgame, we aim to optimize the hybrid beamforming design of the satellite, UAV and IRS, with the goal of minimizing the total transmit power, while guaranteeing that the downlink received signal-to-interference-plus-noise ratio (SINR) surpasses the minimum communication threshold. We propose an alternating optimization scheme, in which the Cauchy–Schwarz inequality, the AoA-based discretization method, and nonsmooth penalty functions are employed to alternately obtain the optimal satellite beamforming vector, UAV beamforming vector and IRS phase matrix when the other variables are fixed. Through our analytical and numerical results, the proposed beamforming scheme achieves a reduction of 16.9% in average power consumption compared with other benchmark schemes when obtaining the same anti-jamming performance.

Unmanned aerial vehicle (UAV) plays an important role in wireless communication systems, due to the additional degree of freedom realized from its flexible deployment. Driven by this advantage and considering the security issue, this paper aims to investigate UAV-enabled proactive eavesdropping over distributed transmit beamforming-based suspicious communications. Specifically, for the suspicious system, there are multiple suspicious clusters aiming to communicate with the suspicious destination (D) using mutually orthogonal frequency bands, and distributed transmit beamforming is exploited by each cluster to strengthen the signal receiving quality at D. For the legitimate party, the full-duplex UAV exploits one antenna to jam D and uses the other antenna to overhear the signals of the suspicious clusters concurrently. By resorting to the Laguerre series approximation and the central limit theorem, we first characterize, in closed form, the approximated distributions of the receiving signal-to-interference-noise ratio (SINR) at D and the UAV, which are shown to be very tight. Based on this analysis and considering that the suspicious system works in the delay-limited transmission mode or the delay-sensitive transmission mode, we aim to maximize the minimum eavesdropping success probability of the UAV for those suspicious communications links, by jointly adjusting the UAV’s deployment and jamming power allocations over different frequency bands. The problem is highly non-convex. To tackle this, we develop an alternative optimization framework and further a novel and low-complexity solution in the high SNR regime to the optimization problem. Simulation results show the effectiveness of our proposed schemes compared to competitive benchmarks.

This paper studies information surveillance over wireless-powered suspicious multiuser communications, where multiple suspicious transmitters (STs) first harvest wireless energy from the suspicious power beacon (PB) in phase I and then communicate with the suspicious destination (SD) in phase II over mutually orthogonal channels, and there is a legitimate monitor (M) aiming to overhear the suspicious signals of the STs based on wireless-powered cooperative jamming. Specifically, the jammers first harvest energy from M in phase I and then interfere with the SD in phase II. Considering the fairness issue, M aims to maximize the minimum eavesdropping success probability of these suspicious signals, by jointly optimizing its transmit power in phase I and the jammers’ power allocations in phase II. To solve the problem, first we strictly prove that M should exhaust its maximum power for the energy transfer, even the additional energy can be harvested by the STs to enhance their transmit power and rate. Then, the general successive convex approximation (SCA) technique and one low-complexity solution are respectively proposed to optimize the jammers’ power allocations. Further, the closed-form jamming power allocations are derived in the high signal-to-noise ratio range to reveal some interesting insights. The joint deployments of M and the jammers are also investigated to enhance the eavesdropping performance. Simulation results show the effectiveness of our proposed schemes compared to competitive benchmarks.

This paper investigates a cognitive information surveillance scenario, where a half-duplex legitimate monitor (E) aims to eavesdrop the frequency division multiple access based suspicious downlink broadcasting communication, which shares the same spectrum with a primary downlink broadcasting system. Specifically, the suspicious transmitter (ST) employs water-filling power allocation over all orthogonal frequency bands (FBs) to maximize the sum suspicious communication rate of all suspicious receivers (SRs), while the primary transmitter (PT) purposely maximizes the minimum communication rate among the primary receivers (PRs). Under this setup, E picks up certain FBs to jam and eavesdrops over the remaining FBs, and intends to control its transmit jamming beamforming over the jammed FBs to deliberately interfere with the corresponding PRs and SRs, such that the PT and the ST will reactively reallocate their power to facilitate E's eavesdropping. Our objective is to maximize the sum instantaneous (ergodic) eavesdropping rate at E, by jointly optimizing its jamming set and the corresponding jamming beamformers. The optimization problem is non-convex and its approximate and sub-optimal solutions are provided by some convex optimization procedures, along with the optimal and greedy jamming set selections. Results show the effectiveness of our proposed jamming-assisted eavesdropping compared to competitive benchmarks.

We investigate proactive eavesdropping with one half-duplex legitimate monitor (E) in unmanned aerial vehicle (UAV)-enabled suspicious relaying systems, which consist of a suspicious transmitter (ST), a suspicious UAV-based relay (SU) and a suspicious destination (SD). Under this setup, we propose two jamming-assisted eavesdropping strategies, namely, “Eavesdrop-Then-Jam” (ETJ) and “Jam-Then-Eavesdrop” (JTE), to maximize the eavesdropping throughput of E. Specifically, for ETJ, E first eavesdrops the suspicious signal of the ST in the ST-SU phase and then jams the SD in the SU-SD phase; for JTE, E first jams the SU in the ST-SU phase and then eavesdrops the suspicious signal of the SU in the SU-SD phase.However, in both strategies, as the jamming power of E (independent variable) changes, the SU can also adaptively adjust its deployment (dependent variable) to maximize the benefit of the suspicious system. Facing this challenge, we can only employ the undesirable exhaustive search over both the independent and dependent variables to achieve the optimal solution. To decrease the complexity, we further derive a tight approximation of the Lambert function and then develop easy-to-implement algorithms to find the sub-optimal solutions. Numerical results demonstrate the effectiveness of our proposed strategies compared to conventional passive eavesdropping.

This letter studies jamming-assisted proactive eavesdropping over multiple orthogonal suspicious links with a wireless-powered monitor. Considering the shortage of energy, the monitor adopts the power splitting technique to divide each of the received suspicious signals into two parts for information decoding and energy harvesting, and then exploits the accumulated energy to jam over those suspicious links, to reduce the corresponding suspicious communication rate and facilitate the eavesdropping of the monitor itself. Our objective is to maximize the minimum eavesdropping success probability of the monitor for all received suspicious signals, by jointly optimizing its power splitting ratios and jamming power allocations. The formulated problem involves the complex Lambert function and is highly non-convex. To tackle this challenge, first we derive a very tight approximation of the Lambert function based on its key property. Then, we employ the general successive convex approximation (SCA)-based technique to iteratively find a locally optimal solution. Moreover, we propose a parallel coordinate descent (PCD)-based low-complexity algorithm to obtain a sub-optimal solution. Numerical results show the effectiveness of our proposed schemes compared to competitive benchmarks.

This letter studies proactive eavesdropping with one legitimate monitor (E) in the classical single-hop suspicious communication. Specifically, unlike all previous works that ignored the suspicious channel training phase and just considered the jamming power optimization of E in the suspicious data transmission phase to facilitate eavesdropping, this letter advances the research by comprehensively investigating the jamming power allocation of E in both phases, with the purpose of maximizing its eavesdropping success probability. Under this setup, we first derive an exact expression of the objective and reveal the existence of a fundamental trade-off in deciding the jamming power allocation, for which a simple one-dimensional search is employed to find the optimal solution. To simplify the analysis, we further derive a tight approximation of the objective and then develop a very fast alternating optimization algorithm to find the sub-optimal jamming power allocation. Moreover, we extend our analysis to the case where the channel state information is available at the suspicious transmitter via channel feedback. Simulation results demonstrate the effectiveness of our proposed scheme compared to competitive benchmarks.

Abstract A proactive spectrum monitoring and spectrum monitoring data (SMD) transmission coexistence system is investigated in dynamic spectrum sharing network, where a spectrum monitor (SM) aims to monitor the spectrum information from the electromagnetic signal sent by the suspicious transmitter to the suspicious destination by using a portion of its antennas and transmit its SMD to the spectrum data fusion center by using the rest antennas. The SMD transmission of the SM can also be employed to jam the suspicious destination to realise the effective proactive spectrum monitoring. Thus, the precoding and antenna selection scheme are jointly designed at the SM to maximise the sum of the achievable spectrum monitoring rate and the SMD transmission rate. The scenario that the SM can assess the statistical channel state information of the suspicious links based on the spectrum information obtained from the spectrum database as the spectrum management node is then considered. The spectrum monitoring/SMD transmission success probability is also derived, and a trade‐off between them is further revealed. Simulation results show that the proposed schemes can obtain higher sum rate of the spectrum monitoring and the SMD transmission, which also verifies its effectiveness and above “security‐reliability” trade‐off.

This letter studies the legitimate surveillance with one half-duplex legitimate monitor (E) over the suspicious multichannel relaying system, where the suspicious transmitter (ST) and relay (SR) implement the optimal power allocation over all orthogonal channels under a joint sum-power constraint to maximize the communication rate of the suspicious system. Under this setup and considering that SR operates in amplify-and-forward (AF) or decode-and-forward (DF) mode, E aims to i) determine the optimal set of sub-channels for jamming and ii) optimize its jamming power over these sub-channels in order to deliberately force ST and SR reallocating power to the unjammed sub-channels to the most, so as to increase the perceived power of E over the unjammed sub-channels and then maximize the eavesdropping rate. In particular, for the AF case, the formulated problem is non-convex, for which the slack variables are introduced and successive convex approximation is exploited. For the DF case, some insights are provided for the optimization process. Results present the considerable gain of the proposed strategy compared to intuitive schemes.