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

Seung-Hwan Seo, Seong-Gyun Choi, Ji-Hee Yu, Yoon-Ju Choi, Ki-Chang Tong, Min-Hyeok Choi, Yeong-Gyun Jung, Hyoung-Kyu Song and Young-Hwan You ()
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Seung-Hwan Seo: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
Seong-Gyun Choi: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
Ji-Hee Yu: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
Yoon-Ju Choi: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
Ki-Chang Tong: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
Min-Hyeok Choi: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
Yeong-Gyun Jung: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
Hyoung-Kyu Song: Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
Young-Hwan You: Department of Computer Engineering, Sejong University, Seoul 05006, Republic of Korea

Mathematics, 2025, vol. 13, issue 17, 1-17

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.

Keywords: reconfigurable intelligent surface (RIS); multi-BS; beamforming; graph neural network (GNN); computational efficiency; parameter efficiency (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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