From Gaussian Multiterminal Source Coding to Distributed Karhunen–Loève Transform

Jun Chen

Department of Electrical and Computer Engineering,
McMaster University

Characterizing the rate-distortion region of Gaussian multiterminal source coding is a longstanding open problem in network information theory. In this talk, I will show how to obtain new conclusive results for this problem using nonlinear analysis and convex relaxation techniques. A byproduct of this line of research is an efficient algorithm for determining the optimal distributed Karhunen–Loève transform in the high-resolution regime, which partially settles a question posed by Gastpar, Dragotti, and Vetterli. I will also introduce a generalized version of the Gaussian multiterminal source coding problem where the source-encoder connections can be arbitrary. It will be demonstrated that probabilistic graphical models offer an ideal mathematical language for describing how the performance limit of a generalized Gaussian multiterminal source coding system depends on its topology, and more generally they can serve as the long-sought platform for systematically integrating the existing achievability schemes and converse arguments. The architectural implication of our work for low-latency lossy source coding will also be discussed. This talk is based on joint work with Jia Wang, Farrokh Etezadi, and Ashish Khisti.

Published on April 11th, 2018

Last updated on April 26th, 2018

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