1. 5G無線通訊 (5G Wireless) : 小型基地台、異質網路、mmWave 通訊系統、巨量智慧型天線、感知無線電
2. 軟體定義行動通訊網路 (Software-Defined Mobile Network): Mobile Edge Computing、 Cloud-Radio Access Network, Virtualization for Multiple Radio Access Techniques
Li-Chun Wang received Ph. D. degree from the Georgia Institute of Technology, Atlanta, in 1996. From 1996 to 2000, he was with AT&amp;T Laboratories, where he was a Senior Technical Staff Member in the Wireless Communications Research Department. Currently, he is the Chair Professor of the Department of Electrical and Computer Engineering and the Director of Big Data Research Center of of National Chiao Tung University in Taiwan.Dr. Wang was elected to the IEEE Fellow in 2011 for his contributions to cellular architectures and radio resource management in wireless networks. He was the co-recipients of IEEE Communications Society Asia-Pacific Board Best Award (2015), Y. Z. Hsu Scientific Paper Award (2013), and IEEE Jack Neubauer Best Paper Award (1997). He won the Distinguished Research Award of Ministry of Science and Technology in Taiwan twice (2012 and 2016). He is currently the associate editor of IEEE Transaction on Cognitive Communications and Networks. His current research interests are in the areas of software-defined mobile networks, heterogeneous networks, and data-driven intelligent wireless communications. He holds 23 US patents, and have published over 300 journal and conference papers, and co-edited a book, “Key Technologies for 5G Wireless Systems,” (Cambridge University Press 2017).
Title：Machine Learning Interference Management for High-Precision Wireless Communication
In this talk, we discuss how machine learning algorithms can address the performance
issues of high-capacity ultra-dense small cells in an environment with dynamical
traffic patterns and time-varying channel conditions. We introduce a bi adaptive self-
organizing network (Bi-SON) to exploit the power of data-driven resource
management in ultra-dense small cells (UDSC). On top of the Bi-SON framework, we
further develop an affinity propagation unsupervised learning algorithm to improve
energy efficiency and reduce interference of the operator deployed and the plug-and-
play small cells, respectively. Finally, we discuss the opportunities and challenges of
reinforcement learning and deep reinforcement learning (DRL) in more decentralized,
ad-hoc, and autonomous modern networks, such as Internet of things (IoT), vehicle –
to-vehicle networks, and unmanned aerial vehicle (UAV) networks.