Artificial Intelligence Enabled Wireless Networking for 5G and Beyond: Recent Advances and Future Challenges

被引:163
作者
Wang, Cheng-Xiang [1 ,2 ]
Di Renzo, Marco [3 ]
Stanczak, Slawomir [4 ,5 ]
Wang, Sen [6 ]
Larsson, Erik G. [7 ]
机构
[1] Southeast Univ, Nanjing, Peoples R China
[2] Purple Mt Labs, Nanjing, Peoples R China
[3] Univ Paris Saclay, CNRS, Cent Supelec, Lab Signaux & Syst, Paris, France
[4] Heinrich Hertz Inst Nachrichtentech Berlin GmbH, Fraunhofer Inst Telecommun, Wireless Commun & Networks Dept, Berlin, Germany
[5] Tech Univ Berlin, Network Informat Theory, Berlin, Germany
[6] Heriot Watt Univ, Robot & Autonomous Syst, Edinburgh, Midlothian, Scotland
[7] Linkoping Univ, Linkoping, Sweden
基金
欧盟地平线“2020”; 国家重点研发计划; 中国国家自然科学基金;
关键词
Artificial intelligence; Channel estimation; Massive MIMO; 5G mobile communication; Loss measurement; Wireless networks; CHANNEL ESTIMATION;
D O I
10.1109/MWC.001.1900292
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
5G wireless communication networks are currently being deployed, and B5G networks are expected to be developed over the next decade. AI technologies and, in particular, ML have the potential to efficiently solve the unstructured and seemingly intractable problems by involving large amounts of data that need to be dealt with in B5G. This article studies how AI and ML can be leveraged for the design and operation of B5G networks. We first provide a comprehensive survey of recent advances and future challenges that result from bringing AI/ML technologies into B5G wireless networks. Our survey touches on different aspects of wireless network design and optimization, including channel measurements, modeling, and estimation, physical layer research, and network management and optimization. Then ML algorithms and applications to B5G networks are reviewed, followed by an overview of standard developments of applying AI/ML algorithms to B5G networks. We conclude this study with future challenges on applying AI/ML to B5G networks.
引用
收藏
页码:16 / 23
页数:8
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