Author:
Oshima Koji, ,Yamamoto Daisuke,Yumoto Atsuhiro,Kim Song-Ju,Ito Yusuke,Hasegawa Mikio, ,
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>
Publisher
American Institute of Mathematical Sciences (AIMS)
Subject
Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine
Reference82 articles.
1. Y. LeCun, Y. Bengio, G. Hinton, Deep learning, Nature, 521 (2015), 436–444. doi: 10.1038/nature14539.
2. Google AI Blog, AlphaGo: Mastering the ancient game of Go with Machine Learning, 2016. Available from: https://ai.googleblog.com/2016/01/alphago-mastering-ancient-game-of-go.html.
3. The 3rd Generation Partnership Project (3GPP). Available from: https://www.3gpp.org/specifications/specifications.
4. H. Yang, A. Alphones, Z. Xiong, D. Niyato, J. Zhao, K. Wu, Artificial-intelligence-enabled intelligent 6G networks, IEEE Network, 34 (2020), 272–280. doi: 10.1109/MNET.011.2000195.
5. A. Goldsmith, S. Chua, Adaptive coded modulation for fading channels, IEEE Trans. Commun., 46 (1998), 595–602. doi: 10.1109/26.668727.
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献