Next Article in Journal
Wave Load Reduction and Tranquility Zone Formation Using an Elastic Plate and Double Porous Structures for Seawall Protection
Previous Article in Journal
A Cascaded Data-Driven Approach for Photovoltaic Power Output Forecasting
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Mathematics
Volume 13
Issue 17
10.3390/math13172732
mathematics-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

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

by
Seung-Hwan Seo
1,2,
Seong-Gyun Choi
1,2,
Ji-Hee Yu
1,2,
Yoon-Ju Choi
1,2,
Ki-Chang Tong
1,2,
Min-Hyeok Choi
1,2,
Yeong-Gyun Jung
1,2,
Hyoung-Kyu Song
1,2 and
Young-Hwan You
3,*
1
Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea
2
Department of Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea
3
Department of Computer Engineering, Sejong University, Seoul 05006, Republic of Korea
*
Author to whom correspondence should be addressed.
Mathematics 2025, 13(17), 2732; https://doi.org/10.3390/math13172732 (registering DOI)
Submission received: 30 July 2025 / Revised: 20 August 2025 / Accepted: 22 August 2025 / Published: 25 August 2025
Versions Notes

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. reconfigurable intelligent surface (RIS); multi-BS; beamforming; graph neural network (GNN); computational efficiency; parameter efficiency.

Share and Cite

MDPI and ACS Style

Seo, S.-H.; Choi, S.-G.; Yu, J.-H.; Choi, Y.-J.; Tong, K.-C.; Choi, M.-H.; Jung, Y.-G.; Song, H.-K.; You, Y.-H. Graph Neural Network-Based Beamforming Optimization for Multi-BS RIS-Aided Communication Systems. Mathematics 2025, 13, 2732. https://doi.org/10.3390/math13172732

AMA Style

Seo S-H, Choi S-G, Yu J-H, Choi Y-J, Tong K-C, Choi M-H, Jung Y-G, Song H-K, You Y-H. Graph Neural Network-Based Beamforming Optimization for Multi-BS RIS-Aided Communication Systems. Mathematics. 2025; 13(17):2732. https://doi.org/10.3390/math13172732

Chicago/Turabian Style

Seo, Seung-Hwan, 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. 2025. "Graph Neural Network-Based Beamforming Optimization for Multi-BS RIS-Aided Communication Systems" Mathematics 13, no. 17: 2732. https://doi.org/10.3390/math13172732

APA Style

Seo, S.-H., Choi, S.-G., Yu, J.-H., Choi, Y.-J., Tong, K.-C., Choi, M.-H., Jung, Y.-G., Song, H.-K., & You, Y.-H. (2025). Graph Neural Network-Based Beamforming Optimization for Multi-BS RIS-Aided Communication Systems. Mathematics, 13(17), 2732. https://doi.org/10.3390/math13172732

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop