Deep Learning Based Joint Beamforming Design in IRS-Assisted Secure Communications

Abstract

In this paper, physical layer security (PLS) in an intelligent reflecting surface (IRS) assisted multiple-input multiple-output multiple-antenna eavesdropper (MIMOME) system is studied. In particular, we consider a practical scenario without instantaneous channel state information (CSI) of the eavesdropper and assume that the eavesdropping channel is a Rayleigh channel. To deal with the complexity of currently available IRS-assisted PLS schemes, we propose a low-complexity deep learning (DL) based approach to design transmitter beamforming and IRS jointly, where precoding vector and phase shift matrix are used to minimize the secrecy outage probability. Simulation results demonstrate that the proposed DL-based approach can achieve a similar performance of that with conventional alternating optimization (AO) algorithms with a significantly low computational complexity.