Online machine learning algorithms to optimize performances of complex wireless communication systems

Koji Oshima; Daisuke Yamamoto; Atsuhiro Yumoto; Song-Ju Kim; Yusuke Ito; Mikio Hasegawa; Koji Oshima; Daisuke Yamamoto; Atsuhiro Yumoto; Song-Ju Kim; Yusuke Ito; Mikio Hasegawa

doi:10.3934/mbe.2022097

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 2: 2056-2094. doi: 10.3934/mbe.2022097

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Research article Special Issues

Online machine learning algorithms to optimize performances of complex wireless communication systems

1.
Innovation Design Initiative, National Institute of Information and Communications Technology, Koganei, Tokyo, Japan
2.
Department of Electrical Engineering, Tokyo University of Science, Katsushika, Tokyo, Japan
3.
SOBIN Institute LLC, Kawanishi, Hyogo, Japan

Received: 31 October 2021 Accepted: 16 December 2021 Published: 27 December 2021

Data-driven and feedback cycle-based approaches are necessary to optimize the performance of modern complex wireless communication systems. Machine learning technologies can provide solutions for these requirements. This study shows a comprehensive framework of optimizing wireless communication systems and proposes two optimal decision schemes that have not been well-investigated in existing research. The first one is supervised learning modeling and optimal decision making by optimization, and the second is a simple and implementable reinforcement learning algorithm. The proposed schemes were verified through real-world experiments and computer simulations, which revealed the necessity and validity of this research.
- wireless communication systems,
- machine learning,
- cross layer optimization,
- optimization algorithm,
- cognitive radio,
- complex systems,
- multi-armed bandit problem,
- reinforcement learning
Citation: Koji Oshima, Daisuke Yamamoto, Atsuhiro Yumoto, Song-Ju Kim, Yusuke Ito, Mikio Hasegawa. Online machine learning algorithms to optimize performances of complex wireless communication systems[J]. Mathematical Biosciences and Engineering, 2022, 19(2): 2056-2094. doi: 10.3934/mbe.2022097

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Abstract

Data-driven and feedback cycle-based approaches are necessary to optimize the performance of modern complex wireless communication systems. Machine learning technologies can provide solutions for these requirements. This study shows a comprehensive framework of optimizing wireless communication systems and proposes two optimal decision schemes that have not been well-investigated in existing research. The first one is supervised learning modeling and optimal decision making by optimization, and the second is a simple and implementable reinforcement learning algorithm. The proposed schemes were verified through real-world experiments and computer simulations, which revealed the necessity and validity of this research.

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