Graph Neural Networks Meet Wireless Communications: Motivation, Applications, and Future Directions
It addresses the integration of GNNs into wireless communications for next-generation systems, but it is an incremental survey paper without new results.
This article provides a comprehensive overview of the interplay between graph neural networks (GNNs) and wireless communications, discussing applications in both directions (GNN4Com and Com4GNN) and highlighting potential research directions.
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.