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

Wang, Cheng-Xiang (author): Southeast Univ, Peoples R China; Purple Mt Labs, Peoples R China

Stanczak, Slawomir (author): Heinrich Hertz Inst Nachrichtentech Berlin GmbH, Germany; Tech Univ Berlin, Germany

Larsson, Erik G (author): Linköpings universitet,Kommunikationssystem,Tekniska fakulteten

(creator_code:org_t)

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2020
2020
English.
In: IEEE wireless communications. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1536-1284 .- 1558-0687. ; 27:1, s. 16-23

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Abstract Subject headings

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.

By the author/editor: Wang, Cheng-Xian ...; Di Renzo, Marco; Stanczak, Slawom ...; Wang, Sen; Larsson, Erik G

About the subject

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Electrical Engin ...; and Communication Sy ...

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