Deep Reinforcement Learning for Optimizing RIS-Assisted HD-FD Wireless Systems

TL;DR

This paper introduces a deep reinforcement learning approach to optimize reconfigurable intelligent surface configurations in wireless systems, significantly enhancing data rates and reducing computational complexity in both half-duplex and full-duplex modes.

Contribution

It presents the first application of DRL for RIS-assisted MISO systems operating in both HD and FD modes, optimizing rate and complexity.

Findings

DRL significantly improves system rate over non-optimized scenarios.

The proposed method reduces computational complexity in HD MISO systems.

It achieves higher rates with fewer steps per episode compared to conventional DRL.

Abstract

This letter investigates the reconfigurable intelligent surface (RIS)-assisted multiple-input single-output (MISO) wireless system, where both half-duplex (HD) and full-duplex (FD) operating modes are considered together, for the first time in the literature. The goal is to maximize the rate by optimizing the RIS phase shifts. A novel deep reinforcement learning (DRL) algorithm is proposed to solve the formulated non-convex optimization problem. The complexity analysis and Monte Carlo simulations illustrate that the proposed DRL algorithm significantly improves the rate compared to the non-optimized scenario in both HD and FD operating modes using a single parameter setting. Besides, it significantly reduces the computational complexity of the downlink HD MISO system and improves the achievable rate with a reduced number of steps per episode compared to the conventional DRL algorithm.