Energy-Efficient Multi-RIS-Aided Rate-Splitting Multiple Access: A Graph Neural Network Approach

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

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

This letter explores energy efficiency (EE) maximization in a downlink multiple-input single-output (MISO) reconfigurable intelligent surface (RIS)-aided multiuser system employing rate-splitting multiple access (RSMA). The optimization task entails base station (BS) and RIS beamforming and RSMA common rate allocation with constraints. We propose a graph neural network (GNN) model that learns beamforming and rate allocation directly from the channel information using a unique graph representation derived from the communication system. The GNN model outperforms existing deep neural network (DNN) and model-based methods in terms of EE, demonstrating low complexity, resilience to imperfect channel information, and effective generalization across varying user numbers.

Original languageEnglish (US)
Pages (from-to)2003-2007
Number of pages5
JournalIEEE Wireless Communications Letters
Volume13
Issue number7
DOIs
StatePublished - 2024
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Keywords

  • beamforming
  • graph neural network
  • rate-splitting multiple access
  • Reconfigurable intelligent surface

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