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

被引：0
|
作者：
Oshima K. [1 ,2 ]
Yamamoto D. [2 ]
Yumoto A. [2 ]
Kim S.-J. [3 ]
Ito Y. [2 ]
Hasegawa M. [2 ]
机构：
[1] Innovation Design Initiative, National Institute of Information and Communications Technology, Tokyo, Koganei
[2] Department of Electrical Engineering, Tokyo University of Science, Katsushika, Tokyo
[3] SOBIN Institute LLC, Kawanishi, Hyogo
来源：
Mathematical Biosciences and Engineering | 2021年 / 19卷 / 02期
关键词：
Cognitive radio; Complex systems; Cross layer optimization; Machine learning; Multi-armed bandit problem; Optimization algorithm; Reinforcement learning; Wireless communication systems;
D O I：
10.3934/MBE.2022097
中图分类号：
学科分类号：
摘要：
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. © 2022 the Author(s), licensee AIMS Press.
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页码：2056 / 2094
页数：38
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