Graph Representation Learning for Wireless Communications

TL;DR

This paper explores how graph neural networks can be applied to wireless networks, leveraging their graph-structured nature to improve network optimization tasks with initial promising results.

Contribution

It provides an overview of graph learning concepts and demonstrates the potential of GNNs in wireless network applications through use cases and initial results.

Findings

GNNs can effectively model wireless network structures.

Initial results show promise in access point selection for MIMO systems.

Graph learning offers new avenues for wireless network optimization.

Abstract

Wireless networks are inherently graph-structured, which can be utilized in graph representation learning to solve complex wireless network optimization problems. In graph representation learning, feature vectors for each entity in the network are calculated such that they capture spatial and temporal dependencies in their local and global neighbourhoods. Graph neural networks (GNNs) are powerful tools to solve these complex problems because of their expressive representation and reasoning power. In this paper, the potential of graph representation learning and GNNs in wireless networks is presented. An overview of graph learning is provided which covers the fundamentals and concepts such as feature design over graphs, GNNs, and their design principles. Potential of graph representation learning in wireless networks is presented via few exemplary use cases and some initial results on the GNN-based access point selection for cell-free massive MIMO systems.