A New Data Processing Inequality and Its Applications in Distributed Source and Channel Coding
Abstract
In the distributed coding of correlated sources, the problem of characterizing the joint probability distribution of a pair of random variables satisfying an n-letter Markov chain arises. The exact solution of this problem is intractable. In this paper, we seek a single-letter necessary condition for this n-letter Markov chain. To this end, we propose a new data processing inequality on a new measure of correlation by means of spectrum analysis. Based on this new data processing inequality, we provide a single-letter necessary condition for the required joint probability distribution. We apply our results to two specific examples involving the distributed coding of correlated sources: multi-terminal rate-distortion region and multiple access channel with correlated sources, and propose new necessary conditions for these two problems.
Cite
@article{arxiv.cs/0611017,
title = {A New Data Processing Inequality and Its Applications in Distributed Source and Channel Coding},
author = {W. Kang and S. Ulukus},
journal= {arXiv preprint arXiv:cs/0611017},
year = {2016}
}
Comments
45 pages, 3 figures, submitted to IEEE Trans. Information Theory