Deep Reinforcement Learning-Based Intelligent Reflecting Surface Optimization for TDD Multi-User MIMO Systems
Fengyu Zhao, Wen Chen, Ziwei Liu, Jun Li, Qingqing Wu
Abstract
In this letter, we investigate the discrete phase shift design of the intelligent reflecting surface (IRS) in a time-division duplexing (TDD) multi-user multiple-input-multiple-output (MIMO) system. We modify the design of deep reinforcement learning (DRL) scheme so that we can maximizing the average downlink data transmission rate free from the sub-channel channel state information (CSI). Based on the characteristics of the model, we modify the “proximal policy optimization (PPO)” algorithm and integrate gated recurrent unit (GRU) to tackle the non-convex optimization problem. Simulation results show that the performance of the proposed PPO-GRU surpasses the benchmarks in terms of performance, convergence speed, and training stability.