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
Source abstract
<abstract><p>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.</p></abstract>
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