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

This study 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.