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 language | English (US) |
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Pages (from-to) | 2003-2007 |
Number of pages | 5 |
Journal | IEEE Wireless Communications Letters |
Volume | 13 |
Issue number | 7 |
DOIs | |
State | Published - 2024 |
Externally published | Yes |
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