Title: On source-channel separation over networks
One of the important architectural insights from information theory is the Shannon source-channel separation theorem. For point-to-point channels, the separation theorem shows that one can compress a source separately and have a digital interface with the noisy channel coding; and that such an architecture is (asymptotically in block size) optimal. Therefore the importance of this is that one can 'layer' the architecture by separating the data compression into bits and the 'physical layer' of coding for noise. The optimality of this attractive architecture is known to break down in networks, for example for broadcast channels or multiple access channels. Nonetheless, this architecture is the basis for network layering in many of the current network architectures.
A natural question is to study the 'cost' of separation, that is, how much do we lose through separation, and cases where we can demonstrate that separation is indeed optimal. We show that the separation approach is optimal in two general network scenarios, and is approximately optimal in a third general scenario. We will also connect the approximate optimality of separation to work on multiple description data compression. We will also mention special situations where one can demonstrate explicit optimal (hybrid) source-channel coding strategies.
Parts of this work are joint with Chao Tian, Shlomo Shamai and Jun Chen.
Bio: Suhas N. Diggavi received a B. Tech. degree in electrical engineering from the Indian Institute of Technology, Delhi, India, and the Ph.D. degree in electrical engineering from Stanford University, Stanford, CA. After completing his Ph.D., he was a Principal Member Technical Staff in the Information Sciences Center, AT&T Shannon Laboratories, Florham Park, NJ. After that he was on the faculty of the School of Computer and Communication Sciences, EPFL, where he directed the Laboratory for Information and Communication Systems (LICOS). He joined UCLA as Professor of Electrical Engineering in 2010.
He is a recipient of the 2006 IEEE Donald Fink prize paper award, 2005 IEEE Vehicular Technology Conference best paper award and the Okawa foundation research award. He was an associate editor for Communication Letters and was a guest editor for a special issue in the IEEE Journal on Special Topics in Signal Processing. He is currently an associate editor for the ACM/IEEE Transactions on Networking and the IEEE Transactions on Information Theory (Shannon theory). He has 8 issued patents.