Empower 5G Wireless with Big Data Analytics and Ultra-Dense Small Cells Networks
5G wireless aims to achieve the goals of 1,000 times higher system capacity, five times reduced end-to-end latency, 10 times longer battery life, and 100 more connected devices compared to the current 4G wireless systems. Some 5G key technology enablers, such as new millimeter wave transmissions and massive MIMO, have shown promising results from the viewpoint of the physical layer. However, the challenge for 5G wireless is not just delivering one gigabyte per user daily, but personalizing every bit and being profitably as well as timely. Therefore, we are motivated to envision a 5G approach from the network and application aspects, driven by human centric intelligent Internet of Things (i-IoT) application trends. In this 5G approach, key enabling technologies consist of big data empowered self-organized networks and human activity recognition, and smart management of ultra-dense small cells networks (UDN). To change the current wireless networks from reactive to proactive, and from cell-centric to human (device)-centric, software-defined networking (SDN) will play a key role for 5G wireless in dynamic base station reconfiguration for reducing operational expenditure (OPEX), and dynamic service chaining for enhancing customers’ quality of experience (QoE), respectively. We show some promising results based on the proposed technology enablers, and discuss new research directions towards the intelligent broadband 5G wireless in the future.
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- 161 Warren Street
- Newark, New Jersey
- United States 07102
- Building: ECEC
- Room Number: 202
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Amit Patel, a.j.patel@ieee.org
Dr. Ajay K. Poddar, Ph.: 201-560-3806, email:akpoddar@ieee.org
Prof. D. Misra, Ph: 973-596-5739, email: dmisra@njit.edu
Prof. Edip Niver, Ph: 973-596-3542, email: edip.niver@njit.edu
- Co-sponsored by NJIT, AP03/MTT17, CAS04/ED15
Speakers
Prof. Li-Chun Wang of National Chiao Tung University, Taiwan
Empower 5G Wireless with Big Data Analytics and Ultra-Dense Small Cells Networks
5G wireless aims to achieve the goals of 1,000 times higher system capacity, five times reduced end-to-end latency, 10 times longer battery life, and 100 more connected devices compared to the current 4G wireless systems. Some 5G key technology enablers, such as new millimeter wave transmissions and massive MIMO, have shown promising results from the viewpoint of the physical layer. However, the challenge for 5G wireless is not just delivering one gigabyte per user daily, but personalizing every bit and being profitably as well as timely. Therefore, we are motivated to envision a 5G approach from the network and application aspects, driven by human centric intelligent Internet of Things (i-IoT) application trends. In this 5G approach, key enabling technologies consist of big data empowered self-organized networks and human activity recognition, and smart management of ultra-dense small cells networks (UDN). To change the current wireless networks from reactive to proactive, and from cell-centric to human (device)-centric, software-defined networking (SDN) will play a key role for 5G wireless in dynamic base station reconfiguration for reducing operational expenditure (OPEX), and dynamic service chaining for enhancing customers’ quality of experience (QoE), respectively. We show some promising results based on the proposed technology enablers, and discuss new research directions towards the intelligent broadband 5G wireless in the future.
Biography:
Li-Chun Wang (M'96 -- SM'06 -- F'11) received the B.S. degree from National Chiao Tung University, Taiwan, R. O. C. in 1986, the M.S. degree from National Taiwan University in 1988, and the Ms. Sci. and Ph. D. degrees from the Georgia Institute of Technology , Atlanta, in 1995, and 1996, respectively, all in electrical engineering.
From 1990 to 1992, he was with the Telecommunications Laboratories of Chunghwa Telecom Co. In 1995, he was affiliated with Bell Northern Research of Northern Telecom, Inc., Richardson, TX. 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 the current Chairman of the same department. His current research interests are in the areas of radio resource management and cross-layer optimization techniques for wireless systems, heterogeneous wireless network design, and cloud computing for mobile applications.
Dr. Wang won the Distinguished Research Award of National Science Council, Taiwan in 2012, and was elected to the IEEE Fellow grade in 2011 for his contributions to cellular architectures and radio resource management in wireless networks. He was a co-recipient(with Gordon L. Stuber and Chin-Tau Lea) of the 1997 IEEE Jack Neubauer Best Paper Award for his paper ``Architecture Design, Frequency Planning, and Performance Analysis for a Microcell/Macrocell Overlaying System," IEEE Transactions on Vehicular Technology, vol. 46, no. 4, pp. 836-848, 1997. He has published over 200 journal and international conference papers. He served as an Associate Editor for the IEEE Trans. on Wireless Communications from 2001 to 2005, the Guest Editor of Special Issue on "Mobile Computing and Networking" for IEEE Journal on Selected Areas in Communications in 2005, "Radio Resource Management and Protocol Engineering in Future Broadband Networks" for IEEE Wireless Communications Magazine in 2006, and “Networking Challenges in Cloud Computing Systems and Applications,” for IEEE Journal on Selected Areas in Communications in 2013, respectively. He is holding 10 US patents.
Email:
Address:Department of Electrical and Computer Engineering, National Chiao Tung University , , Hsinchu, Taiwan
Prof. Li-Chun Wang of National Chiao Tung University, Taiwan
Empower 5G Wireless with Big Data Analytics and Ultra-Dense Small Cells Networks
Biography:
Email:
Address:Hsinchu, Taiwan
Agenda
10:30 AM: Refreshments and Networking
11:00 AM: Seminar in ECE 202
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