A New Look at Quadratic Gaussian Network Compression

Speaker: Dr. Aaron Wagner

The celebrated theorem of Slepian and Wolf describes the rates needed for separate encoders to losslessly compress correlated sources without cooperating. The quadratic Gaussian analogue of this problem has been open since the 1970's. For the Gaussian problem, I will show that a simple layered compression architecture is optimal, and thereby determine the rate region, if (a) there are two encoders, or (b) only one of the sources is of interest to the decoder and the sources satisfy a certain tree structure. I will also show that the layered architecture is not optimal for a simple generalization of these cases.
This is joint work with Saurabh Tavildar and Pramod Viswanath.


Aaron Wagner is an Assistant Professor in the School of Electrical and Computer Engineering at Cornell University. He received the B.S. degree from the University of Michigan, Ann Arbor, and the M.S. and Ph.D. degrees from the University of California, Berkeley. During the 2005-2006 academic year, he was a Postdoctoral Research Associate in the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign and a Visiting Assistant Professor in the School of Electrical and Computer Engineering at Cornell. He has received the NSF CAREER award, the David J. Sakrison Memorial Prize from the U.C. Berkeley EECS Dept., and the Bernard Friedman Memorial Prize in Applied Mathematics from the U.C. Berkeley Dept. of Mathematics.

Presented On: Feb 23rd, 2007
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