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).
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
