TY - JOUR
T1 - Additive non-Gaussian noise channels
T2 - 2005 IEEE International Symposium on Information Theory, ISIT 05
AU - Guo, Dongning
AU - Shamai, Shlomo
AU - Verdú, Sergio
N1 - Funding Information:
This work was supported in part by the U.S. National Science Foundation under Grant NCR-0074277; and through collaborative participation in the Communications and Networks Consortium sponsored by the U.S. Army Research Laboratory under the Collaborative Technology Alliance Program, Cooperative Agreement DAAD19-01-2-0011. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation thereon.
Publisher Copyright:
© 2005 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
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U2 - 10.1109/ISIT.2005.1523430
DO - 10.1109/ISIT.2005.1523430
M3 - Conference article
AN - SCOPUS:84872524343
VL - 2005-January
JO - IEEE International Symposium on Information Theory - Proceedings
JF - IEEE International Symposium on Information Theory - Proceedings
SN - 2157-8097
M1 - 1523430
Y2 - 4 September 2005 through 9 September 2005
ER -