Tutorials
Machine Learning for/in Wireless Networks
Abstract of the tutorial: This tutorial aims to provide fundamentals of machine learning. Specifically, deep supervised learning and reinforcement learning are introduced. One special feature of this tutorial is that it is specialized to wireless networks. The outline is as follows: (1) Issues in microwave and mmWave WLANs, (2) Reinforcement learning towards spatial reuse technique in IEEE 802.11ax, (3) From basics to practice: Supervised learning, and (4) Deep Learning in/for wireless networks. In detail, we address how to apply the machine learning techniques to challenges in wireless networks, that is channel allocation, received power prediction, and handover.
Koji Yamamoto received the Ph.D. degrees in informatics from Kyoto University in 2005. He is currently an Associate Professor in communications and computer engineering with the Graduate School of Informatics, Kyoto University.
Takayuki Nishio received the Ph.D. degrees in informatics from Kyoto University in 2013. He is currently an Assistant Professor in communications and computer engineering with the Graduate School of Informatics, Kyoto University.
