Deep Reinforcement Learning-Based Intelligent Reflecting Surface Optimization for TDD Multi-User MIMO Systems

IF 4.6 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Wireless Communications Letters Pub Date : 2023-08-03 DOI:10.1109/LWC.2023.3301496

Fengyu Zhao;Wen Chen;Ziwei Liu;Jun Li;Qingqing Wu

引用次数: 0

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.

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基于深度强化学习的TDD多用户MIMO系统智能反射面优化

在这封信中，我们研究了时分双工（TDD）多用户多输入多输出（MIMO）系统中智能反射面（IRS）的离散相移设计。我们修改了深度强化学习（DRL）方案的设计，以便在不受子信道状态信息（CSI）影响的情况下最大化平均下行链路数据传输速率。基于模型的特点，我们修改了“近端策略优化（PPO）”算法，并集成了门控递归单元（GRU）来解决非凸优化问题。仿真结果表明，所提出的PPO-GRU在性能、收敛速度和训练稳定性方面都超过了基准。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Wireless Communications Letters Engineering-Electrical and Electronic Engineering

CiteScore

12.30

自引率

6.30%

发文量

481

期刊介绍： IEEE Wireless Communications Letters publishes short papers in a rapid publication cycle on advances in the state-of-the-art of wireless communications. Both theoretical contributions (including new techniques, concepts, and analyses) and practical contributions (including system experiments and prototypes, and new applications) are encouraged. This journal focuses on the physical layer and the link layer of wireless communication systems.