Source-Channel Communication in Networks: Separation Theorems and Beyond
Professor Jun Chen
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, through a suitable application of reduction techniques, source-channel separation theorems can be used to prove the optimality of non-separation based schemes (e.g., hybrid coding schemes) and determine performance limits even in certain scenarios where the separation architecture is suboptimal! However, in general it is possible to obtain stronger converse results using a method termed source-channel correspondence. In particular, I will discuss how this method can be used to derive a new outer bound on the admissible source region for the problem of losslessly transmitting correlated sources over a broadcast channel.
This talk is based on joint work with Kia Khezeli, Lin Song, Chao Tian, Suhas Diggavi, and Shlomo Shamai.
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.