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无线所系列报告——Dr. Jun Chen
发表时间:2015-06-19 阅读次数:1152次

Source-Channel Communication in Networks: Separation Theorems and Beyond

 

Dr. Jun Chen

McMaster University

 

Time: 2pm ~ 3pm, June 23 (Tue) 2015

Venue: SEIEE 5-301A 无线所会议室

 

Abstract: I will first present several results on the optimality of the source-channel separation architecture for lossy source coding in general networks. These results are shown without explicitly characterizing the achievable joint source-channel coding distortion region or the achievable separation-based coding distortion region. I will then consider the lossy source broadcast problem and show that a method termed source-channel correspondence can be used to derive non-trivial or even tight converse results in scenarios where the separation architecture is suboptimal. This talk is based on joint work with Kia Khezeli, Lin Song, Chao Tian, Suhas Diggavi, and Shlomo Shamai.

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Bio:  Jun Chen received the B.E. degree with honors in communication engineering from Shanghai Jiao Tong University, Shanghai, China, in 2001 and the M.S. and Ph.D. degrees in electrical and computer engineering from Cornell University, Ithaca, NY, in 2004 and 2006, respectively. He was a Postdoctoral Research Associate in the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign, Urbana, IL, from September 2005 to July 2006, and a Postdoctoral Fellow at the IBM Thomas J. Watson Research Center, Yorktown Heights, NY, from July 2006 to August 2007. Since September 2007 he has been with the Department of Electrical and Computer Engineering at McMaster University, Hamilton, ON, Canada, where he is currently an Associate Professor. His research interests include information theory, wireless communications, and signal processing. He received several awards for his research, including the Josef Raviv Memorial Postdoctoral Fellowship in 2006, the Early Researcher Award from the Province of Ontario in 2010, and the IBM Faculty Award in 2010. He is currently serving as an Associate Editor for Shannon Theory for the IEEE Transactions on Information Theory.

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