Secure transmission design for RIS-aided symbiotic radio networks: A DRL approach

Abstract

In this paper, we investigate a Reconfigurable Intelligent Surface (RIS)-assisted secure Symbiosis Radio (SR) network to address the information leakage of the primary transmitter (PTx) to potential eavesdroppers. Specifically, the RIS serves as a secondary transmitter in the SR network to ensure the security of the communication between the PTx and the Primary Receiver (PRx), and simultaneously transmits its information to the PTx concurrently by configuring the phase shifts. Considering the presence of multiple eavesdroppers and uncertain channels in practical scenarios, we jointly optimize the active beamforming of PTx and the phase shifts of RIS to maximize the secrecy energy efficiency of RIS-supported SR networks while satisfying the quality of service requirement and the secure communication rate. To solve this complicated non-convex stochastic optimization problem, we propose a secure beamforming method based on Proximal Policy Optimization (PPO), which is an efficient deep reinforcement learning algorithm, to find the optimal beamforming strategy against eavesdroppers. Simulation results show that the proposed PPO-based method is able to achieve fast convergence and realize the secrecy energy efficiency gain by up to 22% when compared to the considered benchmarks.

Keywords

Symbiotic radio / Reconfigurable intelligent surface / Robust transmission / Deep reinforcement learning / Proximal policy optimization

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