Strong data processing inequalities in power-constrained Gaussian channels
Author(s)Calmon, Flavio P.; Polyanskiy, Yury; Wu, Yihong
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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).
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Proceedings of the 2015 IEEE International Symposium on Information Theory (ISIT)
Institute of Electrical and Electronics Engineers (IEEE)
Calmon, Flavio P., Yury Polyanskiy, and Yihong Wu. “Strong Data Processing Inequalities in Power-Constrained Gaussian Channels.” 2015 IEEE International Symposium on Information Theory (ISIT) (June 2015).
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