An Overview on the Application of Graph Neural Networks in Wireless Networks

TL;DR

This paper reviews how graph neural networks are constructed and applied to optimize various wireless network problems, highlighting recent progress and future research directions.

Contribution

It provides a comprehensive overview of GNN construction methods and their applications in wireless networks, including resource allocation and emerging fields.

Findings

GNNs effectively model wireless network data structures.

Applications improve resource management and system performance.

Future trends include integration with new wireless technologies.

Abstract

In recent years, with the rapid enhancement of computing power, deep learning methods have been widely applied in wireless networks and achieved impressive performance. To effectively exploit the information of graph-structured data as well as contextual information, graph neural networks (GNNs) have been introduced to address a series of optimization problems of wireless networks. In this overview, we first illustrate the construction method of wireless communication graph for various wireless networks and simply introduce the progress of several classical paradigms of GNNs. Then, several applications of GNNs in wireless networks such as resource allocation and several emerging fields, are discussed in detail. Finally, some research trends about the applications of GNNs in wireless communication systems are discussed.