Secrecy Energy Efficiency Maximization for Distributed Intelligent-Reflecting-Surface-Assisted MISO Secure Communications

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

In this paper, we investigate an unmanned aerial vehicle (UAV) mounted intelligent reflecting surface (IRS) assisted systems, where an access point (AP) sends short packets to multiple users with the help of an UAV mounted IRS (U-IRS). The maximum average age of information (AoI) among users is minimized by jointly optimizing the time interval allocation, power allocation and UAV trajectory. However, the formulated problem is difficult to solve due to the non-convex objective function and coupled variables. Thus, we propose an alternating optimization (AO) based algorithm by dividing the problem into three sub-problems, which thus can be efficiently solved via the successive convex approximation (SCA) and block coordinate descent (BCD) techniques. Simulation results show that the proposed U-IRS design can significantly improve the AoI performance as compared to the UAV relay design and the packet error rate (PER) minimized U-IRS design. Furthermore, we find that increasing the number of IRS elements has a compromise effect on the AoI performance. On the one hand, it helps to achieve higher beamforming gain for further improving the transmission reliability and thus better compensating for the packet error owing to using short packet. On the other hand, it leads to higher channel eatimation overhead for reaping the beamforming gain, which however introduces unfavorable larger delay. As a result, while the IRS elements number exceeds a certain value, the AoI performance no more improves but degrades instead.

Intelligent reflecting surface (IRS) is a promising solution to build a programmable wireless environment for future communication systems, in which the reflector elements steer the incident signal in fully customizable ways by passive beamforming. In this paper, an IRS-aided secure spatial modulation (SM) is proposed, where the IRS perform passive beamforming and information transfer simultaneously by adjusting the on-off states of the reflecting elements. We formulate an optimization problem to maximize the average secrecy rate (SR) by jointly optimizing the passive beamforming at IRS and the transmit power at transmitter under the consideration that the direct pathes channels from transmitter to receivers are obstructed by obstacles. As the expression of SR is complex, we derive a newly fitting expression (NASR) for the expression of traditional approximate SR (TASR), which has simpler closed-form and more convenient for subsequent optimization. Based on the above two fitting expressions, three beamforming methods, called maximizing NASR via successive convex approximation (Max-NASR-SCA), maximizing NASR via dual ascent (Max-NASR-DA) and maximizing TASR via semi-definite relaxation (Max-TASR-SDR) are proposed to improve the SR performance. Additionally, two transmit power design (TPD) methods are proposed based on the above two approximate SR expressions, called Max-NASR-TPD and Max-TASR-TPD. Simulation results show that the proposed Max-NASR-DA and Max-NASR-SCA IRS beamformers harvest substantial SR performance gains over Max-TASR-SDR. For TPD, the proposed Max-NASR-TPD performs better than Max-TASR-TPD. Particularly, the Max-NASR-TPD has a closed-form solution.

This work investigates the potential of the aerial intelligent reflecting surface (AIRS) in secure communication, where an intelligent reflecting surface (IRS) carried by an unmanned aerial vehicle (UAV) is utilized to help the communication between the ground nodes. Specifically, we formulate the joint design of the AIRS's deployment and the phase shift to maximize the secrecy rate. To solve the non-convex objective, we develop an alternating optimization (AO) approach, where the phase shift optimization is solved by the Riemannian manifold optimization (RMO) method, while the deployment optimization is handled by the successive convex approximation (SCA) technique. Furthermore, to reduce the computational complexity of the RMO method, an element-wise block coordinate descent (EBCD) based method is employed. Simulation results verify the effect of AIRS in improving the communication security, as well as the importance of designing the deployment and phase shift properly.

In this work, we investigate a novel intelligent surface-assisted multiuser multiple-input single-output multiple-eavesdropper (MU-MISOME) secure communication network where an intelligent reflecting surface (IRS) is deployed to enhance the secrecy performance and an intelligent transmission surface (ITS)-based transmitter is utilized to perform energy-efficient beamforming. A weighted sum secrecy rate (WSSR) maximization problem is developed by jointly optimizing transmit power allocation, ITS beamforming, and IRS phase shift. To solve this problem, we transform the objective function into an approximated concave form by using the successive convex approximation (SCA) technique. Then, we propose an efficient alternating optimization (AO) algorithm to solve the reformulated problem in an iterative way, where Karush-Kuhn-Tucker (KKT) conditions, the alternating direction method of the multiplier (ADMM), and majorization-minimization (MM) methods are adopted to derive the closed-form solution for each subproblem. Finally, simulation results are given to verify the convergence and secrecy performance of the proposed schemes.

In this article, a dual-function intelligent reflecting surface (IRS) assisted cognitive radio (CR) nonorthogonal multiple access (NOMA) networks for secure transmission is studied. Considering the scenario of both internal and multiple external Eves in the system, our goal is to maximize the sum achievable secrecy rate through jointly optimizing the transmit beamforming of cognitive base station (CBS), the mode selection of each IRS element and the phase vector of IRS, while both the passive dual-function IRS and active dual-function IRS are investigated. For passive dual-function IRS setup, we decouple the proposed nonconvex optimization problem into two subproblems, the transmit beamforming subproblem and the phase vector subproblem. For transmit beamforming subproblem, we first use arithmetic geometric mean (AGM) inequality and linear matrix inequality (LMI) to deal with the nonconvex fraction form in the objective function and in constraints. Then, successive convex approximation (SCA) is applied to iteratively solve it. For the phase vector subproblem, we utilize the penalty function to handle the rank one constraint and the binary constraint. By alternately optimizing these two subproblems, we obtain a local optimal solution. Then, we extend the proposed algorithm to the secure transmission scheme with active dual-function IRS. The simulation results demonstrate that the proposed scheme with the help of a dual-function IRS effectively enhances the security of the CR-NOMA network compared to other benchmark schemes. Moreover, when the maximum transmitting power <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P_{\max }\ge 15$ </tex-math></inline-formula> dBm, the proposed active IRS scheme is superior to the proposed passive IRS scheme.

UAV-mounted IRS assisted wireless powered mobile edge computing systems: Joint beamforming design, resource allocation and position optimization

Combining intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) offers a new degree of freedom to improve the coverage performance. However, it is more challenging to secure the air-ground transmission, due to the line-of-sight (LoS) links established by UAV. Since IRS is a promising solution for wireless environment reconfiguration, in this paper, we propose an aerial IRS-assisted secure transmission design in wireless networks. In particular, an access point (AP) equipped with a uniform planar array serves several single-antenna legitimate users in the presence of multiple single-antenna eavesdroppers, whose precise positions are unknown. An IRS is mounted on the UAV to help establish desired virtual LoS links between the AP and legitimate users, while ensuring their security. We aim to maximize the worst-case sum secrecy rate by jointly optimizing the hovering position of UAV, the transmit beamforming of AP and the phase shifts of IRS, subject to the requirement of minimum rate for legitimate users. To tackle this non-convex problem, we first decompose it into three subproblems, which are transformed into convex ones via successive convex approximation. An alternating algorithm is then proposed to solve them iteratively. Simulation results show the effectiveness of the proposed scheme and the security improvement by the joint optimization.

We consider an intelligent reflecting surface (IRS)-assisted communication system as a cost-effective technique to enhance secrecy rate performance in physical-layer security (PLS) systems. In this letter, the randomized radiation (RR)-based PLS algorithm, which uses active antenna arrays to randomize the radiation pattern, is adopted in IRS through passive beamforming. We show that RR cannot be simply applied to IRS because the secrecy rate is subject to the phase-dependent amplitude variations in IRS. Therefore, we provide an exact and closed-form expression for the secrecy rate of RR using IRS. Based on the analysis, we introduce a constant phase shift into the IRS elements and identify it as a key parameter to optimize the secrecy performance through a genetic algorithm.

This article introduces a novel strategy for wireless communication security utilizing intelligent reflecting surfaces (IRS). The IRS is strategically deployed to mitigate jamming attacks and eavesdropper threats while improving signal reception for legitimate users (LUs) by redirecting jamming signals toward desired communication signals leveraging physical layer security (PLS). By integrating the IRS into the backscatter communication system, we enhance the overall secrecy rate of LU, by dynamically adjusting IRS reflection coefficients and active beamforming at the base station (BS). A design problem is formulated to jointly optimize IRS reflecting beamforming and BS active beamforming, considering time-varying channel conditions and desired secrecy rate requirements. We propose a novel approach based on deep reinforcement learning (DRL) named Deep-PLS. This approach aims to determine an optimal beamforming policy capable of thwarting eavesdroppers in evolving environmental conditions. Extensive simulation studies validate the efficacy of our proposed strategy, demonstrating superior performance compared to traditional IRS approaches, IRS backscattering-based anti-eavesdropping methods, and other benchmark strategies in terms of secrecy performance.

This paper optimizes the energy consumption of the downlink of a multiple-antenna base station (BS) transmitting to several single-antenna users. The BS utilizes simultaneous wireless information and power transfer (SWIPT) while receivers apply power-splitting (PS) with a nonlinear energy harvesting model leading to PS-SWIPT. We use an intelligent reflecting surface (IRS) and propose a joint design to optimize the active data and the BS's energy beamformers, IRS's passive beamformers, and the receivers' PS ratios under perfect and imperfect CSI availability. In particular, the total BS transmit power is minimized while guaranteeing a minimum rate and harvested energy for each receiver. We apply the block coordinate descent (BCD) method to optimize active and passive beamformers iteratively. We enforce the rank-one constraint and solve the corresponding optimization problem via successive convex approximation (SCA) for accurate semidefinite relaxations. Furthermore, we propose a worst-case robust design for the imperfect CSI case and reformulate this problem with infinitely many constraints. With the BCD method, the problem is iteratively solved via semidefinite programming (SDP) and a second sub-problem with a linear objective and quadratic matrix inequalities, which is also solved via SCA. Numerical results show significant improvements (e.g., 30% decrease in transmit power) than those of no-IRS and IRS with random phase shifts.

Intelligent reflecting surface (IRS) is a very promising technology for the development of beyond 5G or 6G wireless communications due to its low complexity, intelligence, and green energy-efficient properties. In this paper, we combined IRS with physical layer security (PLS) to solve the security issue of cognitive radio (CR) networks. Specifically, an IRS-assisted multi-input single-output (MISO) CR wiretap channel was studied. To maximize the secrecy rate of secondary users subject to a total power constraint (TPC) for the transmitter and interference power constraint (IPC) for a single antenna primary receiver (PR) in this channel, an alternating optimization (AO) algorithm is proposed to jointly optimize the transmit covariance R at transmitter and phase shift coefficient Q at IRS by fixing the other as constant. When Q is fixed, R is globally optimized by equivalently transforming the quasi-convex sub-problem to convex one. When R is fixed, bisection search in combination with minorization–maximization (MM) algorithm was applied to optimize Q from the non-convex fractional programming sub-problem. During each iteration of MM, another bisection search algorithm is proposed, which is able to find the global optimal closed-form solution of Q given the initial point from the previous iteration of MM. The convergence of the proposed algorithm is analyzed, and an extension of applying this algorithm to multi-antenna PR case is discussed. Simulations have shown that our proposed IRS-assisted design greatly enhances the secondary user’s secrecy rate compared to existing methods without IRS. Even when IPC is active, the secrecy rate returned by our algorithm increases with transmit power as if there is no IPC at all.

This paper examines the enhancement of secrecy energy efficiency (SEE) in a reconfigurable intelligent surface (RIS)-assisted two-way relay (TWR) system. We first establish a theoretical model for the system's secrecy rate, energy consumption, and SEE, and formulate the SEE maximization problem through the joint design of the RIS phase shifts and beamforming matrix. Using techniques such as weighted minimum mean square error (WMMSE), alternating optimization, and the augmented Lagrange method, we then develop a theoretical framework that identifies locally optimal solutions for the RIS and beamforming settings under unit-modulus and power constraints. The proposed framework is also shown to be applicable to solving the system's secrecy rate maximization problem. To address the computational complexity involved in optimizing the RIS phase shifts, we further propose a suboptimal scheme leveraging the Newton's method, which significantly reduces the computational burden while achieving performance close to the optimal SEE. Extensive numerical results validate the effectiveness of the proposed schemes, showing significant SEE improvements compared to traditional channel-capacity-based secure transmission scheme.

This paper investigates secure broadcast communications in intelligent reflecting surface (IRS)-aided millimeter wave (mmWave) symbiotic radio (SR) networks, where the IRS and the base station (BS) are cooperating. Specifically, the IRS assists secure broadcasting for the BS by smartly reconfiguring the phase shifts of the signals from the BS, while delivering its own information to an Internet-of-Things device (IoD). We aim at maximizing the minimum secrecy rate of multiple users by jointly designing the hybrid precoder at the BS and the passive beamformer at the IRS, subject to the rate constraints of both the multiple users and the IoD. The design is formulated as a non-convex optimization problem, which is intractable. To handle it, we develop a computationally efficient iterative algorithm based on the successive convex approximation, the efficiency and superiority of which have been validated by simulation results.

A Survey of Intelligent Reflecting Surfaces: Performance Analysis, Extensions, Potential Challenges, and Open Research Issues