Intelligent reflective surfaces for enhanced C-V2X physical layer security optimization

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.

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 Surface (IRS) is envisioned as a revolutionary solution for providing high data rate communication, better signal coverage and physical layer security through controlling the transmission environment of electromagnetic (EM) wave. In this paper, based on a single IRS and millimeter channel model, the optimal multi-user transmission sum rate is derived with the maximal ratio combining (MRC) beamformer. The conditions to achieve such a maximum sum rate are expressed as a function of placement of transceivers, number of IRS elements and its spacing. We first transformed the single IRS assisted non-scattering mmWave channel into a high-rank multiple-in multiple-out (MIMO) channel. The result shows that even without rich scattering, each single antenna user can enjoy the maximal passive beamforming gain while keeping spatial orthogonality to leverage the maximal spatial diversity when the corresponding conditions are satisfied. Simulation results verify the existence and accuracy of optimal placement of multi-user transmission and present that the transmission sum rate of IRS channel scales by the number of IRS elements with different spacing settings.

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 investigates physical-layer security (PLS) of a typical wireless-powered communication (WPC) system with the aid of intelligent reflecting surface (IRS) in the presence of a passive eavesdropper for Internet of Things (IoT), and proposes three IRS-aided secure WPC modes. Specifically, in mode-I, the IRS is deployed between hybrid access point (HAP) and wireless user (U) for co-located power station (PS) and access point (AP). In mode-II, the IRS is deployed between AP and U for separate PS and AP, while in mode-III, the IRS is deployed between PS and U for separate PS and AP. For each mode, the optimal phase shift is designed to maximize the reception of energy and information at the legitimate receiver. We comprehensively analyze the performance of each mode, and derive the closed-form expressions of connection outage probability (COP), secrecy outage probability (SOP), and effective secrecy throughput (EST) for each mode, respectively. The theoretical analysis and simulation results reveal that from the perspective of reliability, mode-I can achieve the best COP with the increased number of IRS elements, while mode-II and mode-III have a similar COP. From the perspective of security, mode-II can achieve the best SOP with the increased number of IRS elements, while mode-I and mode-III have a similar SOP. Moreover, under the condition of small transmission power at HAP/PS or small number of IRS elements, mode-I has the best EST, while as the power or the number increases, the EST of mode-II becomes the best one.

Secure communication of intelligent reflecting surface-aided NOMA in massive MIMO networks

Intelligent reflecting surface (IRS) is a promising technology for its capability of enhancing the desired signals and suppressing the interference. In this paper, we introduce IRS into a device-to-device (D2D) underlaying cellular system and investigate its application on physical layer security. The secrecy rate is maximized by jointly designing the received beamforming vector, power allocation and phase shift matrix. We apply the block coordinate descent (BCD) algorithm to decouple the optimization problem, and the semidefinite relaxation (SDR) technique is adopted to optimize the phase shift matrix. Simulation results show that the IRS is able to improve the system secrecy performance of the D2D underlying cellular system.

In this paper, we study the Physical Layer Security (PLS) problems in intelligent reflecting surface (IRS) multiple-input single-output (MISO) communication systems. A novel transmission scheme aimming to minimize the system energy consumption is proposed by jointly optimizing the phase shift matrix at the IRS and the beamforming vectors at the base station (BS). Furthermore, the maximum secrecy capacity is obtained by designing the time allocation between channel training and information transmission. Theory analysis and numerical results validate that the time ratio value depends on the signal-to-noise ratio (SNR) at the intended receiver.KeywordsPhysical layer securityIntelligent reflecting surfaceSecrecy capacityTime allocation

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

This work considers covert communications with the aid of an intelligent reflecting surface (IRS). We jointly design the transmit power and IRS's reflection phase shifts and amplitudes to enhance covert communication performance. To this end, we first derive the total detection error rate at a warden. We then determine the IRS's optimal phase shifts and develop an effective one-dimensional search method to identify the IRS's optimal amplitudes. We show that the IRS can significantly improve the achievable covertness. In addition, optimizing the IRS's amplitudes on top of its phase shifts significantly outperforms fixing them as unit in covert communications.

In this paper, we introduce an intelligent reflecting surface (IRS) to provide a programmable wireless environment for physical layer security. By adjusting the reflecting coefficients, the IRS can change the attenuation and scattering of the incident electromagnetic wave so that it can propagate in the desired way toward the intended receiver. Specifically, we consider a downlink multiple-input single-output (MISO) broadcast system, where the base station (BS) transmits independent data streams to multiple legitimate receivers and keeps them secret from multiple eavesdroppers. By jointly optimizing the beamformers at the BS and reflecting coefficients at the IRS, we formulate a minimum-secrecy-rate maximization problem under various practical constraints on the reflecting coefficients. The constraints capture the scenarios of both continuous and discrete reflecting coefficients of the reflecting elements. Due to the non-convexity of the formulated problem, we propose an efficient algorithm based on the alternating optimization and the path-following algorithm to solve it in an iterative manner. Besides, we show that the proposed algorithm can converge to a local (global) optimum. Furthermore, we develop two suboptimal algorithms with some forms of closed-form solutions to reduce computational complexity. Finally, the simulation results validate the advantages of the introduced IRS and the effectiveness of the proposed algorithms.

We consider a wireless communication system, where a transmitting source is assisted by both a reconfigurable intelligent reflecting surface (IRS) and a decode-and-forward half-duplex relay (hybrid IRS-relay scheme) to communicate with a destination receiver. All devices are equipped with multiple antennas, and transmissions occur in two stages. In stage 1, the source splits the transmit message into two sub-messages, transmitted to the destination and the relay, respectively, using block diagonalization to avoid interference. Both transmissions will benefit from the IRS. In stage 2, the relay re-encodes the received sub-message and forwards it (still through the IRS) to the destination. We optimize power allocations, beamformers, and configurations of the IRS in both stages, in order to maximize the achievable rate at the destination. We compare the proposed hybrid approach with other schemes (with/without relay and IRS), and confirm that high data rate is achieved for the hybrid scheme in case of optimal IRS configurations.

By controlling the beam directions, intelligent re-flecting surfaces (IRSs) are expected to reconstruct wireless propagation environments. In order to fully understand the potential of IRSs to improve cellular network performance, accurate system modeling and analysis of the IRS assisted cellular networks are particularly important. In this paper, using stochastic geometry, we provide an analytical framework for IRS-assisted cellular networks. Particularly, we model the 3D dynamic beamforming of BSs and IRSs based on geometry and radiation patterns to characterize the interference from both BSs and IRSs. Correspondingly, we derive the expression of the coverage prob-ability. Numerical results also show that the deployment of IRSs significantly enhances the system performance compared with traditional cellular networks without IRSs. Besides, we reveal the impact of system parameters related to BSs and IRSs (such as height, mainlobe beamwidth, etc.) on the coverage performance, thereby providing technical guidelines for the deployment of IRSs in the future cellular network.

The paper investigates the security of an intelligent reflecting surface (IRS) assisted unmanned aerial vehicle (UAV) network, where a base station (BS) transmits confidential information to the ground user (GU) via IRS-assisted UAV. The study explores using UAVs with IRS to enhance the security and reliability of wireless communication systems, particularly in the presence of eavesdroppers and friendly jammers. The beamforming at IRS-assisted UAV and UAV trajectory are jointly formulated as a non-convex optimization problem, which is solved by the deep reinforcement learning (DRL) algorithm to maximize the sum secrecy rate of GU with the aid of jammer. We proposed a dual-DDPG (D3PG) algorithm that utilizes the deep deterministic policy gradient (DDPG) structure to effectively address these dual non-convex problems of the UAV-trajectory optimization and the UAV-IRS beamforming optimization. The proposed algorithm's effectiveness and robustness are demonstrated through simulation results, with the IRS significantly enhancing the sum secrecy rate. Extensive simulations show that the proposed DRL-based D3PG scheme outperforms the traditional optimization schemes and ordinary DQN schemes.

In a wireless system with Intelligent Reflective Surfaces (IRS) containing many passive elements, we consider the problem of channel estimation. All the links from the transmitter to the receiver via each IRS elements (or groups) are estimated. We show that the estimation performance are dependent on the setting of the IRS, and design an optimal channel estimation scheme where the IRS elements follow an optimal series of activation patterns. The optimal design is guided by results for the minimum variance unbiased estimation. The IRS setting during the channel estimation period mimics the discrete Fourier transforms. We show theoretically and with simulations that the estimation variance is one order smaller compared to existing methods with on/off IRS activation patterns as proposed in the literature.