Learning Cooperative Beamforming with Edge-Update Empowered Graph Neural Networks

TL;DR

This paper introduces an Edge-GNN model for cooperative beamforming in wireless networks, enabling real-time solutions with better performance and scalability than existing methods by modeling edge relationships.

Contribution

The paper proposes a novel Edge-GNN architecture with edge-update mechanisms, enhancing the modeling of cooperative beamforming problems on wireless network graphs.

Findings

Edge-GNN achieves higher sum rate than state-of-the-art methods.

Edge-GNN significantly reduces computation time.

Model generalizes well to different network sizes.

Abstract

Cooperative beamforming design has been recognized as an effective approach in modern wireless networks to meet the dramatically increasing demand of various wireless data traffics. It is formulated as an optimization problem in conventional approaches and solved iteratively in an instance-by-instance manner. Recently, learning-based methods have emerged with real-time implementation by approximating the mapping function from the problem instances to the corresponding solutions. Among various neural network architectures, graph neural networks (GNNs) can effectively utilize the graph topology in wireless networks to achieve better generalization ability on unseen problem sizes. However, the current GNNs are only equipped with the node-update mechanism, which restricts it from modeling more complicated problems such as the cooperative beamforming design, where the beamformers are on the graph edges of wireless networks. To fill this gap, we propose an edge-graph-neural-network (Edge-GNN) by incorporating an edge-update mechanism into the GNN, which learns the cooperative beamforming on the graph edges. Simulation results show that the proposed Edge-GNN achieves higher sum rate with much shorter computation time than state-of-the-art approaches, and generalizes well to different numbers of base stations and user equipments.