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

页码：2547 / 2565

共 50 条

[31] Graphs, Convolutions, and Neural Networks: From Graph Filters to Graph Neural Networks
Gama, Fernando
Isufi, Elvin
Leus, Geert
Ribeiro, Alejandro
IEEE SIGNAL PROCESSING MAGAZINE, 2020, 37 (06) : 128 - 138
[32] Graph Neural Networks for Distributed Power Allocation in Wireless Networks: Aggregation Over-the-Air
Gu, Yifan
She, Changyang
Quan, Zhi
Qiu, Chen
Xu, Xiaodong
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2023, 22 (11) : 7551 - 7564
[33] Graph Neural Networks for Graph Drawing
Tiezzi, Matteo
Ciravegna, Gabriele
Gori, Marco
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2024, 35 (04) : 4668 - 4681
[34] Graph Mining with Graph Neural Networks
Jin, Wei
WSDM '21: PROCEEDINGS OF THE 14TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, 2021, : 1119 - 1120
[35] Graph Clustering with Graph Neural Networks
Tsitsulin, Anton
Palowitch, John
Perozzi, Bryan
Mueller, Emmanuel
JOURNAL OF MACHINE LEARNING RESEARCH, 2023, 24
[36] DMT of wireless networks: An overview
Vinodh K.
Prakash N.
Lalitha V.
Vijay Kumar P.
Journal of Communications, 2010, 5 (04): : 265 - 281
[37] Wireless sensor networks: An overview
Wireless sensor networks: Una panoramica
Brandolese, C. (brandole@elet.polimi.it), 1600, Associazione Elettrotecnica ed Elettronica Italiana, Via Mauzo Macchi 32, Milano, 20121, Italy (11):
[38] Core and Wireless Networks Overview
Sabnani, Krishan K.
Buddhikot, Milind M.
Guo, Katherine H.
BELL LABS TECHNICAL JOURNAL, 2009, 14 (02) : 1 - 4
[39] A comparison between Recursive Neural Networks and Graph Neural Networks
Di Massa, Vincenzo
Monfardini, Gabriele
Sarti, Lorenzo
Scarselli, Franco
Maggini, Marco
Gori, Marco
2006 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORK PROCEEDINGS, VOLS 1-10, 2006, : 778 - +
[40] Beyond graph neural networks with lifted relational neural networks
Sourek, Gustav
Zelezny, Filip
Kuzelka, Ondrej
MACHINE LEARNING, 2021, 110 (07) : 1695 - 1738

← 1 2 3 4 5 →