IRS-Assisted Proactive Eavesdropping Over Fading Channels Based on Deep Reinforcement Learning

In this letter, we study an intelligent reflecting surface (IRS)-assisted proactive eavesdropping system, where a legitimate monitor (LM) eavesdrops a point-to-point suspicious wireless communication over Rayleigh fading channel with the assistance of IRS. In order to improve the long-term eavesdropping performance of the system, the reflecting ability of IRS is fully exploited, where the IRS's reflecting optimization problem is established. As the proposed problem is non-convex and difficult to solve, a double deep Q-Learning network (DDQN)-based algorithm is proposed to achieve the optimal reflecting beamforming policy. To this end, the optimization problem is transformed into a Markov Decision Process (MDP) and a reward function which can reflect the eavesdropping performance is designed for agent learning. The simulation results show that the proposed DDQN-based approach achieves the average improvement of 11.27% compared with classical deep Q-network (DQN) algorithm, and with the assistance of IRS, the eavesdropping rate of LM increases by 22.61% and 34.92% compared to proactive eavesdropping via spoofing relay (PESR) and proactive eavesdropping via jamming (PEJ) respectively.

Automated summary

In this paper, an intelligent reflecting surface (IRS)-assisted proactive eavesdropping system is studied, where the reflecting ability of IRS is fully exploited to improve the long-term eavesdropping performance. A double deep Q-Learning network (DDQN)-based algorithm is proposed to achieve the optimal reflecting beamforming policy. Simulation results demonstrate that the proposed approach significantly improves the eavesdropping performance compared to traditional algorithms with the assistance of IRS.