Graph Neural Network-Based Joint Beamforming for Hybrid Relay and Reconfigurable Intelligent Surface Aided Multiuser Systems

Bing Jia Chen, Ronald Y. Chang, Feng Tsun Chien, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

Abstract

This letter examines a downlink multiple-input single-output (MISO) system, where a base station (BS) with multiple antennas sends data to multiple single-antenna users with the help of a reconfigurable intelligent surface (RIS) and a half-duplex decode-and-forward (DF) relay. The system's sum rate is maximized through joint optimization of active beamforming at the BS and DF relay and passive beamforming at the RIS. The conventional alternating optimization algorithm for handling this complex design problem is suboptimal and computationally intensive. To overcome these challenges, this letter proposes a two-phase graph neural network (GNN) model that learns the joint beamforming strategy by exchanging and updating relevant relational information embedded in the graph representation of the transmission system. The proposed method demonstrates superior performance compared to existing approaches, robustness against channel imperfections and variations, generalizability across varying user numbers, and notable complexity advantages.

Original languageEnglish (US)
Pages (from-to)1811-1815
Number of pages5
JournalIEEE Wireless Communications Letters
Volume12
Issue number10
DOIs
StatePublished - Oct 1 2023

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Control and Systems Engineering

Keywords

  • Beamforming
  • graph neural network
  • reconfigurable intelligent surface
  • relaying
  • unsupervised learning

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