Malicious jamming presents a pervasive threat to the secure communications, where the challenge becomes increasingly severe due to the growing capability of the jammer allowing the adaptation to legitimate transmissions. This paper investigates the jamming mitigation by leveraging an active reconfigurable intelligent surface (ARIS), where the channel uncertainties are particularly addressed for robust anti-jamming design. Towards this issue, we adopt the Stackelberg game formulation to model the strategic interaction between the legitimate side and the adversary, acting as the leader and follower, respectively. We prove the existence of the game equilibrium and adopt the backward induction method for equilibrium analysis. We first derive the optimal jamming policy as the follower’s best response, which is then incorporated into the legitimate-side optimization for robust anti-jamming design. We address the uncertainty issue and reformulate the legitimate-side problem by exploiting the error bounds to combat the worst-case jamming attacks. The problem is decomposed within a block successive upper bound minimization (BSUM) framework to tackle the power allocation, transceiving beamforming, and active reflection, respectively, which are iterated towards the robust jamming mitigation scheme. Simulation results are provided to demonstrate the effectiveness of the proposed scheme in protecting the legitimate transmissions under uncertainties, and the superior performance in terms of jamming mitigation as compared with the baselines.

Reconfigurable Intelligent Surface (RIS) has emerged as a key technology capable of economically and efficiently reconfiguring wireless communication environments, and has been widely utilized to assist secure transmission in cell-free (CF) system in recent years. However, due to the presence of multiplicative fading effects, traditional passive RIS typically performs poorly when deployed far from the access point (AP) and users. Additionally, eavesdroppers (Eves) are malicious users hidden within the system, making it difficult for the system to acquire their channel state information (CSI). To address above issues, this paper investigates secure transmission of CF system aided by multiple active RISs. By considering no Eves' CSI, we propose an artificial noise (AN) aided transmssion strategy to enhance the secure transmission of system. In this strategy, the system total power is divided into two parts at the APs, one for the signal transmission and one for the AN signaling. To allocate the first part of power, we propose an efficient alternating optimization (AO) algorithm to optimize the active and passive beamformers at APs and RISs by ensuring that the users' quality of service (QoS) for communication is satisfied. To allocate the second part of residual power, we propose equal and unequal power allocation strategies for transmitting artificial noise (AN) so as to degrade Eves' signal reception performance. The simulation results show that compared with the benchmark schemes, our proposed method with the aide of active RISs in CF system can more effectively suppresse the performance of Eves, which in turn significantly enhances the secrecy rate for users in the system.

This paper propose a graph neural network (GNN) framework to achieve physical layer security. We consider the secure communication between a multi-antenna base station and multiple users, in the presence of multiple eavesdroppers, where the GNN-based beamforming is conducted for secure transmissions. Particularly, we reinterpret the networks roles as graph elements and track the inter-user interference through the graph structure, and thus the secrecy rate maximization is obtained through neural network training. Numerical results indicates that the proposed GNN approach approximate the secrecy performance as compared with the conventional optimization techniques, while obtaining the solution in a more efficient manner, with the ability to adapt and scale in dynamic wireless networks.

This paper studies simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided spectrum sharing cell-free (CF) and cognitive radio (CR) combined IoT system, and focuses on enhancing the secondary user’s (SU’s) achievable rate (AR) while guaranteeing the primary user’s lowest AR requirement. Different from the existing studies, we consider a special condition of limited power supply at RIS in this work, in which only a part of the electromagnetic (EM) elements in RIS can function properly due to lack of energy. To this end, we formulate a worst-case SU’s AR optimization problem under both perfect channel state information (CSI) and imperfect CSI conditions. To tackle these two complicated non-convex problems, alternating optimization framework is proposed to jointly optimize the beamformer at primary and secondary transmitters, transmitting/reflecting phase shift as well as EM element selection at STAR-RIS with provable convergence. In particular, semi-definite relaxation (SDR)+successive convex approximation+penalty-convex concave procedure (PCCP) combined algorithm and SDR+PCCP+Dinkelbach combined algorithm are proposed to solve the highly coupled EM element selection and phase shift at RIS under perfect and imperfect CSI cases, respectively. Numerical results verify that given insufficient power supply and RIS, the proposed scheme significantly improves the AR of SUs and outperforms the benchmark schemes of traditional reflecting RIS-aided or no RIS aided case as well as random EM element selection strategies.

This article investigates the physical-layer security for user-centric cell-free massive multiple-input-multiple-output (UC-CF-mMIMO)-enabled Internet of Things (IoT) network with multiple active eavesdroppers (Eves). We assume that each Eve intends to decode the information of the user (UE) closest to it and is intelligent enough to eliminate interference of other UEs perfectly. Two eavesdropping modes of noncolluding and colluding Eves are considered. Additionally, access points (APs) inject artificial noise (AN) into confidential data signal to hamper the eavesdropping of Eves. To assess the secrecy performance of UC-CF-mMIMO-enabled IoT network, we derive the worst case secrecy rate expression. Then, a max-min secrecy rate (MMSR) problem is formulated via joint optimization of AP clustering, AN selection, and power allocation, which aims to maximize the minimal secrecy rate among attacked UEs while adhering to the Quality-of-Service requirement of UEs, the limitation on the number of UEs and AN sent by each AP, and maximum transmit power limitation of APs. Due to the mixed-integer and nonconvex nature, the formulated problem is tackled via employing the${\ell }_{1}$-norm relaxation and successive convex approximation approach. Finally, we obtain a near-optimal solution to the MMSR problem by iteratively solving a sequence of second-order cone programmings. Simulation results demonstrate the superiority of proposed approach by the comparison with some existing schemes.

Passive sensing has garnered substantial attention in e-healthcare and smart home applications for the appealing concept of detecting human occupancy and activities based on channel state information (CSI) in wireless signal transmissions. Despite the promising benefits of passive sensing, what is less mentioned is the privacy issue raised. It is difficult to prevent a communication device from analyzing CSI to learn private environmental information. This paper investigates the use of a reconfigurable intelligent surface (RIS) by a system planner (SP) to assist a user device (UD) Communication and meanwhile Anti its Passive Sensing based on CSI, named RIS-CAPS. We address the fundamental UD passive sensing performance in identifying changes in the environment, called environmental states, by CSI estimation and reveal the tradeoff between communication and system privacy when a RIS is controlled to influence wireless channels. We report a shortboard effect that to anti UD’s sensing, the UD’s communication performance in different environmental states will be determined by the state having the worst channel condition. Furthermore, we analyze and tackle the new RIS configuration optimization problem to improve UD’s communication while constraining its passive sensing performance. A solution algorithm that exploits alternating optimization and semidefinite relaxation techniques is developed to address the challenging RIS configuration optimization. Finally, extensive simulations validate the performance modeling and analysis in our study. Insights into when RIS is more capable to anti passive sensing are provided.

This paper addresses the challenges of “double-fading” effect and coverage limitations encountered by passive intelligent reflecting surface (IRS) by introducing a novel IRS architecture, termed the dual-sided active-intelligent omni-surface (DSA-IOS). This architecture is capable of processing incident signals on both sides with controllable amplitudes and phases. Furthermore, the DSA-IOS is deployed in a multi-cell multiple-input single-output system to alleviate inter-cell interference and combat potential wiretapping from multi-antenna eavesdroppers. Considering eavesdroppers’ statistical channel state information, we introduce a system metric, the expected secrecy rate (ESR), to capture the tradeoff between secrecy rate (SR) and secrecy outage probability (SOP). Our objective is to maximize the system’s expected secrecy energy efficiency by jointly optimizing the beamformers and artificial noise at the base stations and the reflection and transmission coefficients for both sides at the DSA-IOS. To address the design problem, we propose a low-complexity alternating optimization scheme to acquire an effective suboptimal solution. Simulation results demonstrate that the proposed DSA-IOS outperforms other advanced IRS architectures in enhancing secure performance due to additional degrees of freedom for superior resource utilization. Our results also validate that the proposed ESR metric effectively balances the tradeoff between SR and SOP by customizing SOP thresholds for individual users.

In this paper, to address the “double fading” effect and coverage limitations encountered by conventional passive intelligent reflecting surface (IRS), we propose a novel IRS hardware architecture, termed the dual-sided active (DSA)-IRS, which is capable of simultaneously processing dual-sided incident signals with controllable both amplitude and phase. Furthermore, the DSA-IRS is deployed in a multi-cell multiple-input single-output (MISO) system to alleviate inter-cell interference. Our design objective is to maximize the weighted sum-rate (WSR) among all users by jointly optimizing the beamformers at the base stations (BSs) and the reflection and transmission coefficients for both sides at the DSA-IRS, which is formulated as a non-convex optimization problem. To address the problem, we propose an alternating optimization (AO) scheme to obtain an effective suboptimal solution. Simulation results demonstrate that the proposed DSA-IRS outperforms other advanced IRS architectures in enhancing system performance in multi-cell communications due to the additional degrees of freedom for superior resource utilization.

Active reconfigurable intelligent surface (RIS) has attracted a lot of attention due to its ability to drastically change the communication environment by adjusting the phase shift and amplifying the amplitude of signals. In this paper, we consider to apply the active RIS to enhance the performance of the multi-cluster cooperative non-orthogonal multiple access (CNOMA) system. Specifically, in terms of the power consumption, we first formulate a transmit power minimization problem by jointly optimizing the beamforming at the base station, power splitting ratio at cluster heads, power allocation in each cluster, and RIS matrices in direct transmission and cooperative transmission phases. Then, to improve the fair energy efficiency (EE), we solve a minimum EE maximization problem. To tackle the coupling of optimization variables, we propose the block coordinates descent (BCD) based algorithms. By applying the successive convex approximation (SCA), semi-definite relaxation (SDR), arithmetic-geometric mean (AGM) inequality, Schur complement, and convex upper bound substitution methods, the developed approaches are guaranteed to converge to local optimal solutions. Simulations results demonstrate that the proposed algorithms outperform the baseline schemes under passive RIS aided case, non-cooperation case, and no-RIS case in terms of power consumption and energy efficiency. It is also revealed that active RIS is not always superior to passive RIS schemes with a large number of RIS elements in terms of the system power consumption.

Joint direction-of-arrival (DOA) and range estimation based on frequency diverse array (FDA) have been a critical issue in radar detection, tracking, and navigation. This paper first proposes a bidirectional extension frequency diverse coprime array (BE-FDCA) to obtain an extended 2D virtual array with a larger degree of freedom (DOF). Furthermore, on top of BE-FDCA, we introduce a sparse reconstruction algorithm, referred to as SR-BE-FDCA, to avoid time-consuming peak searches. The algorithm includes two key steps: sparse reconstruction of Decoupled Atomic Norm Minimization (DANM) and 2D ESPRIT estimation. With the aid of these designs, our approach achieves more accurate DOA and range estimations. Finally, numerical simulations are implemented to demonstrate the effectiveness of our method.

Reconfigurable intelligent surfaces (RISs) are recognized with great potential to strengthen wireless security, yet the performance gain largely depends on the deployment location of RISs in the network topology. In this article, we consider the anti-eavesdropping communication established through an RIS at a fixed location, as well as an aerial platform mounting another RIS and a friendly jammer to further improve the secrecy. The aerial RIS helps enhance the legitimate signal and the aerial cooperative jamming is strengthened through the fixed RIS. The security gain with aerial reflection and jamming is further improved with the optimized deployment of the aerial platform. We particularly consider the imperfect channel state information issue and address the worst case secrecy for robust performance. The formulated robust secrecy rate maximization problem is decomposed into two layers, where the inner layer solves for reflection and jamming with robust optimization, and the outer layer tackles the aerial deployment through deep reinforcement learning. Simulation results show the deployment under different network topologies and demonstrate the performance superiority of our proposal in terms of the worst case security provisioning as compared with the baselines.

Intelligent reflecting surface (IRS) has been proposed as a very promising technique for beyond 5G and 6G communications. In this paper, we apply IRS to enhance the secure transmission of secondary user in a multi-input multioutput (MIMO) cognitive radio (CR) wiretap channel. Since the study of secure IRS-assisted CR communication is still an open problem, all the existing numerical solutions for enhancing secure communications in non-CR setting as well as non-secure communications in CR setting in the literature fail to this work due to the complicated structure of objective functions as well as the constraints. Therefore, to maximize the secrecy rate of secondary user, an efficient alternating optimization (AO) algorithm is proposed to jointly optimize the transmit covariance at base station and phase shift coefficients at IRS. Simulation results show that our proposed algorithm have fast monotonic convergence as well as better performance on enhancing the secrecy rate than the benchmark schemes.

Research has shown the potential of device-free WiFi sensing for human identification. Each and every human has a unique gait and prior works suggest WiFi devices are able to capture the unique signature of a person's gait. In this article, we show for the first time that the monitored gait could be inconsistent and have mirror-like perturbations when individuals walk through WiFi devices in different directions, provided that the WiFi antenna array is horizontal to the walking path. Such inconsistent mirrored patterns are to negatively affect the uniqueness of gait and accuracy of human identification. Therefore, we propose a system called Gate-ID for accurately identifying individuals' identities irrespective of different walking directions. Gate-ID employs theoretical communication model and real measurements to demonstrate that antenna array orientations and walking directions contribute to the mirror-like patterns in WiFi signals. A novel heuristic algorithm is proposed to infer individual's walking directions. A set of methods are employed to extract and augment the representative spatial-temporal features of gait and enable the system performing irrespective of walking directions. We further propose a novel attention-based deep learning model that fuses various weighted features and ignores ineffective noises to uniquely identify individuals. We implement Gate-ID on commercial off-the-shelf devices. Extensive experiments demonstrate that our system can uniquely identify people with average accuracy of 90.7%-75.7% from a group of 6-20 people, respectively, and improve the accuracy by 12.5%-43.5% compared with baselines.

In this paper, an intelligent reflecting surface (IRS) assisted spectrum sharing underlay cognitive radio (CR) wiretap channel (WTC) is studied, and we aim at enhancing the secrecy rate of secondary user in this channel subject to total power constraint at secondary transmitter (ST), interference power constraint (IPC) at primary receiver (PR) as well as unit modulus constraint at IRS. Due to extra IPC and eavesdropper (Eve) are considered, all the existing solutions for enhancing secrecy rate of IRS-assisted non-CR WTC as well as enhancing transmission rate in IRS-assisted CR channel without eavesdropper fail in this work. Therefore, we propose new numerical solutions to optimize the secrecy rate of this channel under full primary, secondary users’ channel state information (CSI) and three different cases of Eve’s CSI: full CSI, imperfect CSI with bounded estimation error, and no CSI. To solve the difficult non-convex optimization problem, an efficient alternating optimization (AO) algorithm is proposed to jointly optimize the beamformer at ST and phase shift coefficients at IRS. In particular, when optimizing the phase shift coefficients during each iteration of AO, a Dinkelbach based solution in combination with successive approximation and penalty based solution is proposed under full CSI and a penalty convex-concave procedure solution is proposed under imperfect Eve’s CSI. For no Eve’s CSI case, artificial noise (AN) aided approach is adopted to help enhancing the secrecy rate. Simulation results show that our proposed solutions for the IRS-assisted design greatly enhance the secrecy performance compared with the existing numerical solutions with and without IRS under full and imperfect Eve’s CSI. And positive secrecy rate can be achieved by our proposed AN aided approach given most channel realizations under no Eve’s CSI case so that secure communication also can be guaranteed. All of the proposed AO algorithms are guaranteed to monotonic convergence.

In this letter, improving the security of an intelligent reflecting surface (IRS) assisted multiple-input single-output (MISO) communication system is studied. Different from the ideal assumption in existing literatures that full eavesdropper's (Eve's) channel state information (CSI) is available, we consider a more practical scenario without Eve's CSI. To enhance the security of this system given a total transmit power at transmitter (Alice), we propose a joint beamforming and jamming approach, in which a minimum transmit power is firstly optimized at Alice so as to meet the quality of service (QoS) at legitimate user (Bob), and then artificial noise (AN) is emitted to jam the eavesdropper by using the residual power at Alice. Two efficient algorithms exploiting oblique manifold (OM) and minorization-maximization (MM) algorithms, respectively, are developed for solving the resulting non-convex optimization problem. Simulation results have been provided to validate the performance and convergence of the proposed algorithms.

In this article, we consider an intelligent reflecting surface (IRS) assisted Guassian multiple-input multiple-output (MIMO) wiretap channel (WTC), and focus on enhancing its secrecy rate. Due to MIMO setting, all the existing solutions for enhancing the secrecy rate over multiple-input single-output WTC completely fall to this work. Furthermore, all the existing studies are simply based on an ideal assumption that full channel state information (CSI) of eavesdropper (Ev) is available. Therefore, we propose numerical solutions to enhance the secrecy rate of this channel under both full and no Ev's CSI cases. For the full CSI case, we propose a barrier method and one-by-one (OBO) optimization combined alternating optimization (AO) algorithm to jointly optimize the transmit covariance R at transmitter (Tx) and phase shift coefficient Q at IRS. For the case of no Ev's CSI, we develop an artificial noise (AN) aided joint transmission scheme to enhance the secrecy rate. In this scheme, a bisection search (BS) and OBO optimization combined AO algorithm is proposed to jointly optimize R and Q. Such scheme is also applied to enhance the secrecy rate under a special scenario in which the direct link between Tx and receiver (Rx)/Ev is blocked due to obstacles. In particular, we propose a BS and minorization-maximization (MM) combined AO algorithm with slightly faster convergence to optimize R and Q for this scenario. Simulation results have validated the monotonic convergence of the proposed algorithms, and it is shown that the proposed algorithms for the IRS-assisted design achieve significantly larger secrecy rate than the other benchmark schemes under full CSI. When Ev's CSI is unknown, the secrecy performance of this channel also can be enhanced by the proposed AN aided scheme, and there is a trade-off between increasing the quality of service at Rx and enhancing the secrecy rate.

Secure signaling over multiple-input multiple-output (MIMO) wiretap channel (WTC) is studied under interference and transmit power constraints. The classical MIMO WTC model is extended to interference-limited scenarios, so that interference to other users does not exceed a given threshold while ensuring simultaneously no information leakage to an eavesdropper. The operational secrecy capacity of the Gaussian MIMO WTC under interference and transmit power constraints is rigorously established in two forms (a non-convex max problem and a convex-concave max-min problem), to which per-antenna power constraints can be added as well. Optimal signaling directions are characterized in the general case, from which (tight) upper bounds to the rank of optimal transmit covariance matrix are derived. A sufficient condition for the optimality of beamforming and a necessary condition for optimal full-rank signaling are given. Closed-form rank-1 and high-rank solutions are obtained in the case of zero interference constraints. Sufficient and necessary conditions for non-zero secrecy capacity are established. The results are extended to multi-user scenarios. A sufficient and necessary condition for the unbounded growth of the secrecy capacity with transmit power is obtained. The interplay between transmit and interference power constraints is studied, and its significant impact on optimal signaling is demonstrated (so that neither constraint can be absorbed into the other one in general, as was sometimes suggested in the literature). Overall, these results provide insights into fundamental information-theoretic limits and optimal signaling strategies for secure communications under interference constraints.