Joint Active and Passive Beamforming Design in Intelligent Reflecting Surface (IRS)-Assisted Covert Communications: A Multi-Agent DRL Approach.

Abstract

Intelligent Reflecting Surface (IRS), with the potential capability to reconstruct the electromagnetic propagation environment, evolves a new IRS-assisted covert communications paradigm to eliminate the negligible detection of malicious eavesdroppers by coherently beaming the scattered signals and suppressing the signals leakage. However, when multiple IRSs are involved, accurate channel estimation is still a challenge due to the extra hardware complexity and communication overhead. Besides the cross-interference caused by massive reflecting paths, it is hard to obtain the close-formed solution for the optimization of covert communications. On this basis, the paper improves a heterogeneous multi-agent deep deterministic policy gradient (MADDPG) approach for the joint active and passive beamforming (Joint A&P BF) optimization without the channel estimation, where the base station (BS) and multiple IRSs are taken as different types of agents and learn to enhance the covert spectrum efficiency (CSE) cooperatively. Thanks to the ‘centralized training and distributed execution’ feature of MADDPG, each agent can execute the active or passive beamforming independently based on its partial observation without referring to others. Numeral results demonstrate that the proposed deep reinforcement learning (DRL) approach could not only obtain a preferable CSE of legitimate users and a low detection of probability (LPD) of warden, but also alleviate the communication overhead and simplify the IRSs deployment.