Graph Neural Networks Meet Wireless Communications: Motivation, Applications, and Future Directions

TL;DR

This paper reviews how graph neural networks can enhance wireless communications and vice versa, discussing current applications, construction methods, and future research directions in this interdisciplinary field.

Contribution

It provides a comprehensive overview of GNNs in wireless communications, including construction techniques and reciprocal applications, highlighting future research opportunities.

Findings

GNNs are well-suited for wireless communication challenges.

Construction of graphical models is crucial for GNN applications.

Future directions include innovative GNN architectures for wireless systems.

Abstract

As an efficient graph analytical tool, graph neural networks (GNNs) have special properties that are particularly fit for the characteristics and requirements of wireless communications, exhibiting good potential for the advancement of next-generation wireless communications. This article aims to provide a comprehensive overview of the interplay between GNNs and wireless communications, including GNNs for wireless communications (GNN4Com) and wireless communications for GNNs (Com4GNN). In particular, we discuss GNN4Com based on how graphical models are constructed and introduce Com4GNN with corresponding incentives. We also highlight potential research directions to promote future research endeavors for GNNs in wireless communications.