Research article Special Issues

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

  • 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.

    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

    Related Papers:

  • 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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