Deep Reinforcement Learning for RIS-aided Multiuser Full-Duplex Secure Communications with Hardware Impairments

TL;DR

This paper develops a deep reinforcement learning approach to optimize secure communication in RIS-assisted multiuser full-duplex systems with hardware impairments, significantly enhancing secrecy performance in dynamic environments.

Contribution

It introduces a novel DRL-based algorithm for joint beamforming optimization in complex RIS-aided secure systems with hardware impairments, addressing intractable non-convex problems.

Findings

DRL-based algorithm significantly improves sum secrecy rate.

Performance and convergence speed are enhanced with proper neural network parameters.

The method effectively adapts to time-varying environments.

Abstract

In this paper, we investigate a reconfigurable intelligent surface (RIS)-aided multiuser full-duplex secure communication system with hardware impairments at transceivers and RIS, where multiple eavesdroppers overhear the two-way transmitted signals simultaneously, and an RIS is applied to enhance the secrecy performance. Aiming at maximizing the sum secrecy rate (SSR), a joint optimization problem of the transmit beamforming at the base station (BS) and the reflecting beamforming at the RIS is formulated under the transmit power constraint of the BS and the unit modulus constraint of the phase shifters. As the environment is time-varying and the system is high-dimensional, this non-convex optimization problem is mathematically intractable. A deep reinforcement learning (DRL)-based algorithm is explored to obtain the satisfactory solution by repeatedly interacting with and learning from the dynamic environment. Extensive simulation results illustrate that the DRL-based secure beamforming algorithm is proved to be significantly effective in improving the SSR. It is also found that the performance of the DRL-based method can be greatly improved and the convergence speed of neural network can be accelerated with appropriate neural network parameters.