Graph Neural Network-Based Beamforming Optimization for Multi-BS RIS-Aided Communication Systems

Abstract

The optimization of beamforming in multi-base station (multi-BS) reconfigurable intelligent surface (RIS)-aided systems is a challenging task due to its high computational complexity. This paper first demonstrates that an optimized multi-BS system exhibits superior communication performance compared to a centralized large-scale single-BS system. To efficiently solve the complex beamforming problem in the multi-BS environment, this paper proposes a novel optimization solver based on a graph neural network (GNN) that models the physical structure of the system. Experimental results show that the proposed GNN solver finds solutions of higher quality, achieving a 42% performance increase with 45% less total computational complexity compared to a conventional iterative optimization method. Furthermore, when compared to other complex AI models such as transformer and Bi-LSTM, the proposed GNN achieves similar state-of-the-art performance while having less than 1% of the parameters and a fraction of the computational cost. These findings demonstrate that the GNN is a powerful, efficient, and practical solution for beamforming optimization in multi-BS RIS-aided systems, satisfying the demands for performance, computational efficiency, and model compactness.