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

被引：0

作者：

He, Shiwen ^{[1
,2
,3
]}

Xiong, Shaowen ^{[1
]}

Ou, Yeyu ^{[1
]}

Zhang, Jian ^{[1
]}

Wang, Jiaheng ^{[3
,4
]}

Huang, Yongming ^{[3
,4
]}

Zhang, Yaoxue ^{[5
]}

机构：

[1] School of Computer Science and Engineering, Central South University, Changsha, China

[2] National Mobile Communications Research Laboratory, Southeast University, Nanjing,210096, China

[3] Pervasive Communication Research Center, Purple Mountain Laboratories, Nanjing, China

[4] National Mobile Communications Research Laboratory, School of Information Science and Engineering, Southeast University, Nanjing,210096, China

[5] Department of Computer Science and Technology, Tsinghua University, Beijing, China

来源：

IEEE Open Journal of the Communications Society | 2021年 / 2卷

基金：

中国国家自然科学基金;

关键词：

Deep learning - Graph neural networks - Computing power - Learning systems - Resource allocation;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

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. © 2020 IEEE.

引用

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页码：2547 / 2565

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