Additive non-Gaussian noise channels: Mutual information and conditional mean estimation

Dongning Guo, Shlomo Shamai, Sergio Verdú

Research output: Contribution to journalConference articlepeer-review

36 Scopus citations

Abstract

It has recently been shown that the derivative of the input-output mutual information of Gaussian noise channels with respect to the signal-to-noise ratio is equal to the minimum mean-square error. This paper considers general additive noise channels where the noise may not be Gaussian distributed. It is found that, for every fixed input distribution, the derivative of the mutual information with respect to the signal strength is equal to the correlation of two conditional mean estimates associated with the input and the noise respectively. Special versions of the result are given in the respective cases of additive exponentially distributed noise, Cauchy noise, Laplace noise, and Rayleigh noise. The previous result on Gaussian noise channels is also recovered as a special case.

Original languageEnglish (US)
Article number1523430
JournalIEEE International Symposium on Information Theory - Proceedings
Volume2005-January
DOIs
StatePublished - 2005
Event2005 IEEE International Symposium on Information Theory, ISIT 05 - Adelaide, Australia
Duration: Sep 4 2005Sep 9 2005

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics

Fingerprint

Dive into the research topics of 'Additive non-Gaussian noise channels: Mutual information and conditional mean estimation'. Together they form a unique fingerprint.

Cite this