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报告题目:
On Video Streaming in Cognitive Femtocell Networks
 报告人:
Shiwen Mao
Professor of Auburn University
报告时间:
2014-07-04 10:00
报告地点:
1-415,FIT Building
主办单位:
Research Institute of Information Technology (RIIT), Tsinghua University
  简介:

Biography:

Shiwen Mao (S'99-M'04-SM'09) received Ph.D. in electrical and computer engineering from Polytechnic University, Brooklyn, NY. Currently, he is the McWane Associate Professor in the Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA. His research interests include wireless networks and multimedia communications, with current focus on cognitive radio, small cells, mmWave networks, free space optical networks, and smart grid. He is a Distinguished Lecturer of the IEEE Vehicular Technology Society in the Class of 2014. He is on the Editorial Board of IEEE Transactions on Wireless Communications, IEEE Internet of Things Journal, IEEE Communications Surveys and Tutorials, among others. He is the Vice Chair – Letters of the IEEE ComSoc Multimedia Communications Technical Committee. He received the 2013 IEEE ComSoc MMTC Outstanding Leadership Award and the NSF CAREER Award in 2010. He is a co-recipient of The IEEE ICC 2013 Best Paper Award and The 2004 IEEE Communications Society Leonard G. Abraham Prize in the Field of Communications Systems.

Abstract:

Femtocells are shown highly effective on improving network coverage and capacity by bringing base stations closer to mobile users. In this paper, we investigate the problem of streaming scalable videos in femtocell cognitive radio (CR) networks. This is a challenging problem due to the stringent QoS requirements of real-time videos and the new dimensions of network dynamics and uncertainties in CR networks. We develop a framework that captures the key design issues and trade-offs with a stochastic programming problem formulation. In the case of a single FBS, we develop an optimum-achieving distributed algorithm, which is shown also optimal for the case of multiple non-interfering FBS’s. In the case of interfering FBS’s, we develop a greedy algorithm that can compute near-optimal solutions, and prove a closed-form lower bound on its performance. The proposed algorithms are evaluated with simulations, and are shown to outperform three alternativeschemes with considerable margins.

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