We are witnessing the transition into the fifth generation (5G) cellular mobile systems. Is there any need for beyond 5G? A significant change in recent wireless networks is that much more data are collected from various sources, including channels, locations, radio access options, and network states. The availability of this large amount and various types of data can potentially transform the current knowledge-driven mobile network into a more powerful data-driven cognitive and learning-assisted mobile network.
In this talk,we discuss how machine learning algorithms can address the performance issues ofhigh-capacity ultra-dense small cellsin an environment withdynamical traffic patterns and time-varying channel conditions.First, 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 propose a polynomial regression supervised learning, and 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.
Li-Chun Wang (IEEE Fellow)
Dept. Electrical and Computer Engineering,
National Chaio Tung University, Taiwan
Li-Chun Wang (M'96 -- SM'06 -- F'11)received Ph. D. degree from the Georgia Institute of Technology, Atlanta, in 1996. From 1996 to 2000, he was with AT&T Laboratories, where he was a Senior Technical Staff Member in the Wireless Communications Research Department. Since August 2000, he has joined the Department of Electrical and Computer Engineering of National Chiao Tung University in Taiwan and is jointly appointed by Department of Computer Science and Information Engineering of the same university.
Dr. Wang was elected to the IEEE Fellow in 2011 for his contributions to cellular architectures and radio resource management in wireless networks. He won the Distinguished Research Award of National Science Council, Taiwan (2012). 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).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).
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