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

Journal: Mathematical biosciences and engineering : MBE
Published Date:

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.

Authors

  • Koji Oshima
    Innovation Design Initiative, National Institute of Information and Communications Technology, Koganei, Tokyo, Japan.
  • Daisuke Yamamoto
    Department of Electrical Engineering, Tokyo University of Science, Katsushika, Tokyo, Japan.
  • Atsuhiro Yumoto
    Department of Electrical Engineering, Tokyo University of Science, Katsushika, Tokyo, Japan.
  • Song-Ju Kim
    SOBIN Institute LLC, Kawanishi, Hyogo, Japan.
  • Yusuke Ito
    Department of Urology, Yokohama City University Hospital, Yokohama, Kanagawa, Japan.
  • Mikio Hasegawa
    Department of Electrical Engineering, Tokyo University of Science, Katsushika, Tokyo, Japan.