Intelligent Reflecting Surface Optimization for MIMO Communication Using Deep Reinforcement Learning

Kenneth Ikeagu*, Yuan Ding, Chaoyun Song, Muhammad Khandaker

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

This paper focuses on the optimization of the phase shifts of an intelligent reflecting surface (IRS) for an IRS-aided multiple input multiple output (MIMO) communication system. Motivated by the massive success of deep reinforcement learning (DRL) algorithms in handling high-dimensional continuous action spaces and tackling non-convex optimization problems, we propose a deep deterministic policy gradient (DDPG) framework for solving the formulated non-convex optimization problem. Numerical simulations demonstrate the robustness and efficiency of the proposed model in terms of spectral efficiency and algorithm run time when compared to a state-of-the-art scheme.
Original languageEnglish
Title of host publication31st Telecommunications Forum (TELFOR)
PublisherIEEE
ISBN (Electronic)9798350303131
DOIs
Publication statusPublished - 1 Jan 2024
Event31st Telecommunications Forum 2023 - Belgrade, Serbia
Duration: 21 Nov 202322 Nov 2023

Conference

Conference31st Telecommunications Forum 2023
Abbreviated titleTELFOR 2023
Country/TerritorySerbia
CityBelgrade
Period21/11/2322/11/23

Keywords

  • Deep Reinforcement Learning
  • Intelligent Reflecting Surfaces
  • MIMO
  • Passive Beamforming

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Signal Processing
  • Hardware and Architecture
  • Computer Networks and Communications

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