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arXiv 2106.02533cs.NIcs.LG
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基于图的通信网络深度学习研究综述

Graph-based Deep Learning for Communication Networks: A Survey

Tsinghua University(清华大学)

Weiwei Jiang

摘要:

通信网络是现代社会的重要基础设施。仍然有许多挑战没有完全解决,在这个活跃的研究领域不断提出新的解决方案。近年来,基于图的深度学习对网络拓扑建模在解决通信网络中的一系列问题上取得了最新的成果。在本调查中,我们回顾了使用不同的基于图的深度学习模型(例如图卷积和图注意力网络)在不同类型的通信网络(例如无线网络,有线网络和软件定义网络)中的各种问题中快速增长的研究。我们还提出了一个组织良好的列表的问题和解决方案,为每项研究,并确定未来的研究方向。据我们所知,本文是第一篇关注基于图的深度学习方法在涉及有线和无线场景的通信网络中的应用的调查。为了跟踪后续研究,我们创建了一个公共GitHub存储库,相关论文将在其中不断更新。

英文摘要:

Communication networks are important infrastructures in contemporary society. There are still many challenges that are not fully solved and new solutions are proposed continuously in this active research area. In recent years, to model the network topology, graph-based deep learning has achieved the state-of-the-art performance in a series of problems in communication networks. In this survey, we review the rapidly growing body of research using different graph-based deep learning models, e.g. graph convolutional and graph attention networks, in various problems from different types of communication networks, e.g. wireless networks, wired networks, and software defined networks. We also present a well-organized list of the problem and solution for each study and identify future research directions. To the best of our knowledge, this paper is the first survey that focuses on the application of graph-based deep learning methods in communication networks involving both wired and wireless scenarios. To track the follow-up research, a public GitHub repository is created, where the relevant papers will be updated continuously.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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