Machine Learning in Beyond 5G/6G Networks-State-of-the-Art and Future Trends

被引：53

作者：

Rekkas, Vasileios P. ^{[1
]}

Sotiroudis, Sotirios ^{[1
]}

Sarigiannidis, Panagiotis ^{[2
]}

Wan, Shaohua ^{[3
]}

Karagiannidis, George K. ^{[4
]}

Goudos, Sotirios K. ^{[1
]}

机构：

[1] Aristotle Univ Thessaloniki, Sch Phys, ELEDIA AUTH, Thessaloniki 54124, Greece

[2] Univ Western Macedonia, Dept Informat & Telecommun Engn, Kozani 50100, Greece

[3] Zhongnan Univ Econ & Law, Sch Informat & Safety Engn, Wuhan 430073, Peoples R China

[4] Aristotle Univ Thessaloniki, Sch Elect & Comp Engn, Thessaloniki 54124, Greece

来源：

ELECTRONICS | 2021年 / 10卷 / 22期

基金：

中国国家自然科学基金;

关键词：

6G; wireless communications; artificial intelligence; machine learning; RESOURCE-ALLOCATION; HANDOVER MANAGEMENT; WIRELESS NETWORKS; POWER ALLOCATION; BEAM SELECTION; DEEP; OPTIMIZATION; CHALLENGES; PREDICTION; COMMUNICATION;

D O I：

10.3390/electronics10222786

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Artificial Intelligence (AI) and especially Machine Learning (ML) can play a very important role in realizing and optimizing 6G network applications. In this paper, we present a brief summary of ML methods, as well as an up-to-date review of ML approaches in 6G wireless communication systems. These methods include supervised, unsupervised and reinforcement techniques. Additionally, we discuss open issues in the field of ML for 6G networks and wireless communications in general, as well as some potential future trends to motivate further research into this area.

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