  In this paper, we study an intelligent reflecting surface (IRS)-aided
wireless secure communication system for physical layer security, where an IRS
is deployed to adjust its surface reflecting elements to guarantee secure
communication of multiple legitimate users in the presence of multiple
eavesdroppers. Aiming to improve the system secrecy rate, a design problem for
jointly optimizing the base station (BS)'s beamforming and the IRS's reflecting
beamforming is formulated given the different quality of service (QoS)
requirements and time-varying channel condition. As the system is highly
dynamic and complex, and it is challenging to address the non-convex
optimization problem, a novel deep reinforcement learning (DRL)-based secure
beamforming approach is firstly proposed to achieve the optimal beamforming
policy against eavesdroppers in dynamic environments. Furthermore,
post-decision state (PDS) and prioritized experience replay (PER) schemes are
utilized to enhance the learning efficiency and secrecy performance.
Specifically, PDS is capable of tracing the environment dynamic characteristics
and adjust the beamforming policy accordingly. Simulation results demonstrate
that the proposed deep PDS-PER learning-based secure beamforming approach can
significantly improve the system secrecy rate and QoS satisfaction probability
in IRS-aided secure communication systems.
