Cooperative Beamforming and RISs Association for Multi-RISs Aided Multi-Users MmWave MIMO Systems through Graph Neural Networks

TL;DR

This paper introduces a graph neural network-based approach to optimize beamforming and RIS association in multi-RISs mmWave MIMO systems, significantly enhancing system performance and user service quality.

Contribution

It proposes a novel heterogeneous GNN method to jointly optimize beamforming and RIS association, addressing a complex non-convex problem in multi-RISs wireless systems.

Findings

GNN-based method outperforms benchmarks by about 10 times in WSR optimization.

Dynamic RIS association improves user service quality by approximately 30%.

The approach effectively exploits graph topology for system optimization.

Abstract

Reconfigurable intelligent surface (RIS) is considered as a promising solution for next-generation wireless communication networks due to a variety of merits, e.g., customizing the communication environment. Therefore, deploying multiple RISs helps overcome severe signal blocking between the base station (BS) and users, which is also a practical and effective solution to achieve better service coverage. However, reaping the full benefits of a multi-RISs aided communication system requires solving a non-convex, infinite-dimensional optimization problem, which motivates the use of learning-based methods to configure the optimal policy. This paper adopts a novel heterogeneous graph neural network (GNN) to effectively exploit the graph topology in the wireless communication optimization problem. First, we characterize all communication link features and interference relations in our system with a heterogeneous graph structure. Then, we endeavor to maximize the weighted sum rate (WSR) of all users by jointly optimizing the active beamforming at the BS, the passive beamforming vector of the RIS elements, as well as the RISs association strategy. Unlike most existing work, we consider a more general scenario where the cascaded link for each user is not fixed but dynamically selected by maximizing the WSR. Simulation results show that our proposed heterogeneous GNNs perform about 10 times better than other benchmarks, and a suitable RISs association strategy is also validated to be effective in improving the quality services of users by 30%.