Beamforming and Transmit Power Design for Intelligent Reconfigurable Surface-Aided Secure Spatial Modulation

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 letter studies the joint active and passive beamforming in intelligent reflecting surface (IRS)-enhanced wireless powered mobile edge computing (MEC) networks, in which a hybrid access point (HAP) is deployed to charge multiple wireless devices (WDs) and assist in computation of the offloaded tasks from the WDs. An IRS is deployed to enhance the downlink wireless power transfer (WPT) and the uplink tasks offloading (TO). To maximize the systems’ sum computation rate, an optimization problem is formulated by jointly optimizing the time assignment of WPT and TO, the active beamforming at the HAP, the transmit power and computing frequency of the WDs, and the passive beamforming at the IRS. To tackle the non-convex problem, we present an efficient algorithm by utilizing the alternating optimization (AO) and semidefinite relaxation (SDR) techniques. Simulation results show that by deploying IRS in the system, a significant computation rate gain can be achieved. Especially, when the IRS is closer to the WDs or the HAP, the much higher computation rate can be reached.

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

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.

This article investigates energy-efficient secure communication design with the help of multiple phase-adjustable intelligent reflecting surfaces (IRSs). By creating desirable radiation patterns of wireless environment, the IRSs are used to significantly improve the number of bits securely delivered to the destination per energy consumption in Joule, also known as the secrecy energy efficiency (SEE). By manipulating the discrete reflecting coefficients of multiple IRSs and taking advantage of the active beamforming, this article develops an efficient alternating optimization algorithm based on successive convex approximation and penalty-based techniques to effectively fight against multiple eavesdroppers. Simulation results characterize the graceful tradeoff between conflicting performance metrics, i.e., total power consumption and secrecy rate in the multi-IRS-aided secure communication system, and corroborate that incorporating multiple IRSs is beneficial to both the SEE and secrecy rate enhancement compared to existing related benchmarks.

In this paper, we study resource allocation design for secure communication in intelligent reflecting surface (IRS)-assisted multiuser multiple-input single-output (MISO) communication systems. To enhance physical layer security, artificial noise (AN) is transmitted from the base station (BS) to deliberately impair the channel of an eavesdropper. In particular, we jointly optimize the phase shift matrix at the IRS and the beamforming vectors and AN covariance matrix at the BS for maximization of the system sum secrecy rate. To handle the resulting non-convex optimization problem, we develop an efficient suboptimal algorithm based on alternating optimization, successive convex approximation, semidefinite relaxation, and manifold optimization. Our simulation results reveal that the proposed scheme substantially improves the system sum secrecy rate compared to two baseline schemes.

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.

Intelligent reflecting surfaces (IRSs) are a promising technology for future-generation wireless networks by extending coverage region to blind spots and increasing mmWave propagation paths in non-line of sight environments. User association (UA) in dense millimeter wave (mmWave) networks is vital to characterizing connections among base stations (BSs) and mobile users for load balancing, interference management, and maximizing network utility. However, it has yet to be examined thoroughly in a multi-IRS-aided network. This paper presents a new UA scheme that takes cell interference into account for a multi-cell mmWave cellular network aided with multiple IRSs. We formulate a network spectral efficiency maximization problem by jointly optimizing active beamforming (AB) at the BSs, passive beamforming (PB) at the IRSs, and user-BS association with consideration of the impact of IRSs. We then propose a computationally efficient iterative algorithm based on alternating optimization (AO) to resolve this intractable mixed-integer non-convex problem. A fractional programming technique is used to optimize active beamforming at the BSs and passive beamforming at the IRSs, and a penalization method combined with successive convex programming is applied for UA optimization, which is shown to reach the optimal solution. Simulation results show significant performance improvements obtained by the proposed algorithm, providing higher spectral efficiency compared to several benchmark algorithms, while having a low computational complexity.

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.

This article proposes a novel framework of resource allocation in multi-cell intelligent reflecting surface (IRS) aided non-orthogonal multiple access (NOMA) networks, where an IRS is deployed to enhance the wireless service. The problem of joint user association, subchannel assignment, power allocation, phase shifts design, and decoding order determination is formulated for maximizing the achievable sum rate. The challenging mixed-integer non-linear problem is decomposed into an optimization subproblem (P1) with continuous variables and a matching subproblem (P2) with integer variables. In an effort to tackle the non-convex optimization problem (P1), iterative algorithms are proposed for allocating transmission power, designing reflection matrix, and determining decoding order by invoking relaxation methods such as convex upper bound substitution, successive convex approximation, and semidefinite relaxation. In terms of the combinational problem (P2), swap matching-based algorithms are developed for achieving a two-sided exchange-stable state among users, BSs and subchannels. Numerical results demonstrate that: i) the sum rate of multi-cell NOMA networks is capable of being increased by 35% with the aid of the IRS; ii) the proposed algorithms for multi-cell IRS-aided NOMA networks can enjoy 22% higher energy efficiency than conventional NOMA counterparts; iii) the trade-off between spectrum efficiency and coverage area can be tuned by judiciously selecting the location of the IRS.

Intelligent Reflecting Surfaces (IRS) is a groundbreaking innovative technique to boost energy and spectral efficiency in upcoming wireless systems, enabling simultaneous wireless information and power transfer (SWIPT). This paper optimizes Energy Harvesting Efficiency (EHE) in a multi-input multi-output (MIMO) SWIPT system, where an access point (AP)/base station (BS) uses an IRS to serve multi energy receivers (ERs) and information receivers (IRs). We formulate an EHE optimization problem, designing active and passive beamforming while satisfying the AP/BS power constraints, IRS unit-modulus constraints and IR rate demands in a linear EH model. The Linear Matrix Inequalities (LMIs) are approximated for efficient solutions. And second-order cone programming (SOCP) is solved via convex optimization (CVX), while the penalty convex-concave procedure (CCP) guarantees convexity to non-convex problems. Next, an alternating optimization (AO)-reliant iterative algorithm derived sub-optimal solutions with slack variables. According to simulation results, for all ERs within a linear EH design/model, the proposed algorithm improves total EHE by 40% to 45% compared to randomize, No-IRS, and decode-and-forward (DF) relay strategies. Ultimately, highlighting IRS’s benefit in improving quality of service (QoS) by tweaking its elements and AP/BS antennas.

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.

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 investigates the problem of model aggregation for the federated learning aided by multiple intelligent reflecting surfaces (IRSs). Since the local parameters are transmitted over wireless channels, the undesirable propagation error will inevitably deteriorate the performance of global aggregation. The objective of this work is to reduce the signal distortion of over-the-air computation, thus the mean-square-error (MSE) is minimized by optimizing the transmit power at devices, controlling the receive scalar at the base station, and designing the phase shifts at IRSs, subject to the transmit power constraint for devices and the unit-modulus constraint for reflecting elements. To solve this intractable MSE minimization problem (P1), a closed-form expression for transmit power allocation is first derived, and the receive-scaling control subproblem (P2) is addressed by using the semidefinite relaxation. Then, the phase-shifting design subproblem (P3) is reduced to a feasibility-check problem, which is tackled by invoking the penalty method and successive convex approximation. After that, an alternating optimization algorithm is proposed to find a suboptimal solution for the non-linear and non-convex problem. Finally, compare to baselines, simulation results demonstrate that the designed algorithms for multi-IRS enhanced federated learning can converge faster and aggregate model more accurately.

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.