IRS Backscatter Enhancing Against Jamming and Eavesdropping Attacks
Yurui Cao, Sai Xu, Jiajia Liu, Nei Kato
Abstract
This article proposes a novel intelligent reflecting surface (IRS) backscatter enhancing strategy to secure multi-input multioutput (MIMO) transmission in the presence of an eavesdropper and a malicious jammer. To be specific, the IRS is employed to backscatter the jamming signal into the desired signal to enhance the reception of the user. Utilizing this strategy, we maximize the system secrecy rate by jointly designing the reflection coefficients of IRS and active beamforming at the base station (BS). To efficiently handle this nonconvex optimization problem, we adopt an iterative block coordinate descent (BCD)-based algorithm, where the active beamforming is optimized through the Lagrange multiplier method and the backscatter coefficient matrix of IRS is optimized via the majorization-minimization (MM) method. Then, we examine the robustness of the proposed scheme when considering channel estimation errors. Extensive simulations confirm the secrecy performance gains achieved by our proposed strategy and verify its superiority compared to conventional IRS-based physical layer security (PLS) strategy, IRS backscatter-aided anti-eavesdropping strategy, and other baselines.