Beamforming and Load-Balanced User Association in RIS-Aided Systems via Graph Neural Networks

Kun Lin Chan*, Feng Tsun Chien*, Ronald Y. Chang

*此作品的通信作者

研究成果: Conference contribution同行評審

摘要

This paper considers the joint optimization of user association (UA), base station (BS) transmit beamforming, and reconfigurable intelligent surface (RIS) phase adjustment in a RIS-aided multi-BS multi-user-equipment (UE) system with load balancing. The problem is challenging due to variable coupling and nonconvexity. We propose a novel graph neural network (GNN) approach, leveraging permutation equivariance and invariance properties of GNNs for enhanced generalization compared to conventional deep neural network (DNN) methods. By using uplink pilots as input, our proposed GNN method eliminates the need for explicit channel state information (CSI), which can be challenging to acquire in RIS-aided systems. Simulation results demonstrate the superior performance of our GNN-based method over traditional UA strategies in terms of sum rate and load-balancing violation penalty. Moreover, the benefits of deploying RIS are illustrated from a new perspective of facilitating load balancing without substantial compromises in sum-rate performance.

原文English
主出版物標題ICC 2024 - IEEE International Conference on Communications
編輯Matthew Valenti, David Reed, Melissa Torres
發行者Institute of Electrical and Electronics Engineers Inc.
頁面4997-5002
頁數6
ISBN(電子)9781728190549
DOIs
出版狀態Published - 2024
事件59th Annual IEEE International Conference on Communications, ICC 2024 - Denver, 美國
持續時間: 9 6月 202413 6月 2024

出版系列

名字IEEE International Conference on Communications
ISSN(列印)1550-3607

Conference

Conference59th Annual IEEE International Conference on Communications, ICC 2024
國家/地區美國
城市Denver
期間9/06/2413/06/24

指紋

深入研究「Beamforming and Load-Balanced User Association in RIS-Aided Systems via Graph Neural Networks」主題。共同形成了獨特的指紋。

引用此