A New Data Processing Inequality and Its Applications in Distributed Source and Channel Coding
<|reference_start|>A New Data Processing Inequality and Its Applications in Distributed Source and Channel Coding: 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.<|reference_end|>
@article{kang2006a,
    title={A New Data Processing Inequality and Its Applications in Distributed
  Source and Channel Coding},
    author={W. Kang, S. Ulukus},
    journal={arXiv preprint arXiv:cs/0611017},
    year={2006},
    doi={10.1109/TIT.2010.2090211},
    archivePrefix={arXiv},
    eprint={cs/0611017},
    primaryClass={cs.IT math.IT}
}