论文标题
图形神经网络符合无线通信：动机，应用和未来方向
Graph Neural Networks Meet Wireless Communications: Motivation, Applications, and Future Directions
论文作者
论文摘要
作为有效的图形分析工具，图形神经网络（GNN）具有特别适合无线通信的特征和要求的特殊属性，具有良好的下一代无线通信的潜力。本文旨在全面概述GNNS与无线通信之间的相互作用，包括无线通信的GNN（GNN4COM）和GNNS（COM4GNN）的无线通信。特别是，我们根据如何构建图形模型来讨论GNN4COM，并通过相应的激励措施介绍COM4GNN。我们还强调了潜在的研究方向，以促进无线通信中GNN的未来研究努力。
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.
