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.

引用

页码：2056 / 2094

页数：38

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