Beamforming Optimization Methods for Secrecy Rate Maximization in IRS-Aided Wiretap Channel

Intelligent reflecting surfaces (IRSs) have been recognized as a disruptive and revolutionary technology for achieving efficient and secure information transmission. This paper investigates beamforming optimization methods for an IRS-aided MIMO system with an eavesdropper. A secrecy rate maximization beamforming problem subject to both the transmit power constraints at the base station (BS) and the reflection constraints at the IRS is considered. The problem is non-convex and challenging to solve due to the coupled optimization variables. To address this issue, an iterative alternating optimization (IterAO) algorithm is proposed. The method is easy to implement, but it needs more running time to converge. We then propose a projected gradient method (PGM), which is proven to converge to a stable secrecy rate in relatively less time. It is shown that the proposed IterAO and PGM methods converge to almost the same secrecy rate ultimately, but the PGM significantly reduces the computational complexity. In addition, the secrecy rates of the proposed IterAO and PGM are higher than that of the benchmark scheme. It is also shown that the system with an IRS offers significant achievable secrecy rate gains over that without.