Secrecy Rate Maximization for IRS-assisted MISOME Cognitive Radio System

Abstract

In this paper, we consider a multiple-input-single-output multiple-eavesdropper (MISOME) wiretap cognitive radio (CR) wireless communication system, in which intelligent reflecting surface (IRS) and artificial noise (AN) are exploited to enhance the informational confidentiality. We aim to maximize the achievable secrecy rate of the secondary system by jointly optimizing the transmit beamforming vectors of secondary source and the phase shifts of the IRS. Because the formulated optimal problem is non-convex and constitutes difference-convex-type programming, we propose an effective iterative algorithm based on alternating optimization as well as successive convex approximation (SCA) method to obtain a local suboptimal solution. Simulation results validate the effectiveness of the proposed algorithm and show that the secrecy rate is significantly improved with the assistance of the IRS compared with the benchmark schemes without AN or IRS.