Secrecy Energy Efficiency Maximization in Multi-RIS-Aided SWIPT Wireless Network using Deep Reinforcement Learning

Abstract

This paper studies the secrecy energy efficiency (SEE) of a simultaneous wireless information and power transfer (SWIPT) network aided by multiple reconfigurable intelligent surfaces (RIS). The SWIPT network comprises several information decoding receivers (IDRs), and energy harvesting receivers (EHR) served by an access point (AP) supported by several distributed RIS. To effectively define the trade-off between the secrecy rate and energy efficiency of the multi-RIS SWIPT system, an optimization problem is formulated to maximize the SEE by optimizing the transmit beamforming at the AP and the phase shift at each RIS while dynamically controlling each RIS's ON/OFF status. The resultant non-convex optimization problem is solved using a deep reinforcement learning (DRL) framework to design the beamforming policy and a control mechanism for the RISs. Simulation results show that the proposed algorithm enhances the SEE compared to other benchmark schemes.