Theoretical and Natural Science

- The Open Access Proceedings Series for Conferences


Theoretical and Natural Science

Vol. 12, 17 November 2023


Open Access | Article

Application of machine learning in wireless communication

Suhan Hua 1 , Qianhe Wang 2 , Xingyan Xu * 3
1 Shenzhen College of International Education
2 Nanjing University of Aeronautics and Astronautics
3 Beijing University of Posts and Telecommunications

* Author to whom correspondence should be addressed.

Advances in Humanities Research, Vol. 12, 130-135
Published 17 November 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Suhan Hua, Qianhe Wang, Xingyan Xu. Application of machine learning in wireless communication. TNS (2023) Vol. 12: 130-135. DOI: 10.54254/2753-8818/12/20230452.

Abstract

With the rapid development of wireless communication technology, people have put forward higher requirements for the speed and quality of data transmission in wireless communication while enjoying a convenient life. Wireless communication systems are expected to be combined with artificial intelligence to meet these requirements. Machine learning (ML) can rely on different algorithms to process data without explicit programming. It can also optimize wireless systems by solving complex problems that traditional mathematics cannot solve. This paper briefly introduces wireless communication, machine learning, and the necessity of combining machine learning. The potential and applications of machine learning in various aspects of wireless communication, such as channel estimation, spectrum allocation, adaptive interference suppression, etc., are listed. The paper also introduces the various conveniences that machine learning in wireless communication brings to people in practical applications and the potential hazards that improper applications may bring.

Keywords

Wireless Communication, Machine Learning, Application Situation

References

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Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 2023 International Conference on Mathematical Physics and Computational Simulation
ISBN (Print)
978-1-83558-135-3
ISBN (Online)
978-1-83558-136-0
Published Date
17 November 2023
Series
Theoretical and Natural Science
ISSN (Print)
2753-8818
ISSN (Online)
2753-8826
DOI
10.54254/2753-8818/12/20230452
Copyright
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated