@inproceedings{083a871bdb3741ae915d242589693619,
title = "Strong data processing inequalities in power-constrained Gaussian channels",
abstract = "This work presents strong data processing results for the power-constrained additive Gaussian channel. Explicit bounds on the amount of decrease of mutual information under convolution with Gaussian noise are shown. The analysis leverages the connection between information and estimation (I-MMSE) and the following estimation-theoretic result of independent interest. It is proved that any random variable for which there exists an almost optimal (in terms of the mean-squared error) linear estimator operating on the Gaussian-corrupted measurement must necessarily be almost Gaussian (in terms of the Kolmogorov-Smirnov distance).",
author = "Calmon, {Flavio P.} and Yury Polyanskiy and Yihong Wu",
note = "Funding Information: This work is supported in part by the National Science Foundation (NSF) CAREER award under Grant CCF-12-53205, the NSF Grant IIS-1447879 and CCF-1423088 and by the Center for Science of Information (CSoI), an NSF Science and Technology Center, under Grant CCF-09-39370. Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE International Symposium on Information Theory, ISIT 2015 ; Conference date: 14-06-2015 Through 19-06-2015",
year = "2015",
month = sep,
day = "28",
doi = "10.1109/ISIT.2015.7282918",
language = "English (US)",
series = "IEEE International Symposium on Information Theory - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2558--2562",
booktitle = "Proceedings - 2015 IEEE International Symposium on Information Theory, ISIT 2015",
address = "United States",
}