Liu, M., Huang, C., Alhammadi, A., Di Renzo, M., Debbah, M., & Yuen, C. (2025). Beamforming Design and Association Scheme for Multi-RIS Multi-User mmWave Systems Through Graph Neural Networks. IEEE Transactions on Wireless Communications, 24(9), 7940–7954. https://doi.org/10.1109/twc.2025.3563529
Abstract:
Reconfigurable intelligent surface (RIS) is emerging as a promising technology for next-generation wireless communication networks, offering a variety of merits such as the ability to tailor the communication environment. Moreover, deploying multiple RISs helps mitigate severe signal blocking between the base station (BS) and users, providing a practical and efficient solution to enhance the service coverage. However, fully reaping the potential of a multi-RIS aided communication system requires solving a non-convex optimization problem. This challenge motivates the adoption of learning-based methods for determining the optimal policy. In this paper, we introduce a novel heterogeneous graph neural network (GNN) to effectively leverage the graph topology of a wireless communication environment. Specifically, we design an association scheme that selects a suitable RIS for each user. Then, we maximize the weighted sum rate (WSR) of all the users by iteratively optimizing the RIS association scheme, and beamforming designs until the considered heterogeneous GNN converges. Based on the proposed approach, each user is associated with the best RIS, which is shown to significantly improve the system capacity in multi-RIS multi-user millimeter wave (mmWave) communications. Specifically, simulation results demonstrate that the proposed heterogeneous GNN closely approaches the performance of the high-complexity alternating optimization (AO) algorithm in the considered multi-RIS aided communication system, and it outperforms other benchmark schemes. Moreover, the performance improvement achieved through the RIS association scheme is shown to be of the order of 30%.
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Funding Info:
This research / project is supported by the Ministry of Science and Technology (MOST) and Ministry of Finance, China - China National Key Research and Development Program
Grant Reference no. : 2021YFA1000500 and 2023YFB2904804
This research / project is supported by the National Natural Science Foundation of China - NA
Grant Reference no. : 62331023 and 62394292
This research / project is supported by the Zhejiang Provincial Science and Technology Plan - NA
Grant Reference no. : 2024C01033
This research is supported by core funding from: Zhejiang University Global Partnership Fund
Grant Reference no. : NA
This research / project is supported by the European Union - Horizon Europe
Grant Reference no. : COVER (101086228), UNITE (101129618), INSTINCT (101139161), TWIN6G (101182794)
This research / project is supported by the Agence Nationale de la Recherche (ANR) - France 2030 project ANR-PEPR Networks of the Future
Grant Reference no. : NF-YACARI 22-PEFT- 0005
This research / project is supported by the Agence Nationale de la Recherche (ANR) - CHIST-ERA project PASSIONATE
Grant Reference no. : CHIST-ERA-22-WAI-04 and ANR-23- CHR4-0003-01
This research / project is supported by the Ministry of Education, Singapore - Academic Research Fund Tier 2
Grant Reference no. : T2EP50124-0032
This research / project is supported by the Agency for Science, Technology and Research, Singapore - Manufacturing, Trade, and Connectivity Programmatic
Grant Reference no. : M22L1b0110
This research / project is supported by the National Research Foundation, Singapore and Infocomm Media Development Authority - Future Communications Research and Development Programme
Grant Reference no. : FCP-NTU-RG-2024-025