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

研究成果: Article同行評審

摘要

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.

原文English
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頁數1
期刊IEEE Wireless Communications Letters
DOIs
出版狀態Accepted/In press - 2024

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