# Learning Cooperative Beamforming with Edge-Update Empowered Graph Neural Networks

arXiv: https://arxiv.org/abs/2212.08020 · HTML: https://mlsanity.com/paper/2212.08020/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 23 Nov 2022.

## Builds on (most cited first, 8 of 8)

- [Learning to Optimize: Training Deep Neural Networks for Wireless Resource Management](https://mlsanity.com/paper/1705.09412/) (2017)
- [Optimal Wireless Resource Allocation with Random Edge Graph Neural Networks](https://mlsanity.com/paper/1909.01865/) (2019)
- [Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis](https://mlsanity.com/paper/2007.07632/) (2020)
- [Graph Neural Networks for Wireless Communications: From Theory to Practice](https://mlsanity.com/paper/2203.10800/) (2022)
- [Heterogeneous Transformer: A Scale Adaptable Neural Network Architecture for Device Activity Detection](https://mlsanity.com/paper/2112.10086/) (2021)
- [Learning to Beamform in Heterogeneous Massive MIMO Networks](https://mlsanity.com/paper/2011.03971/) (2020)
- [AI Empowered Resource Management for Future Wireless Networks](https://mlsanity.com/paper/2106.06178/) (2021)
- [Cell-Free Massive MIMO for 6G Wireless Communication Networks](https://mlsanity.com/paper/2110.07309/) (2021)

## Abstract (arXiv)

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.