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INEQUALITIES FOR THE DEPENDENT GAUSSIAN NOISE CHANNELS BASED ON FISHER INFORMATION AND COPULAS

Published online by Cambridge University Press:  07 February 2019

Fatemeh Asgari ,
Mohammad Hossein Alamatsaz Open the ORCID record for Mohammad Hossein Alamatsaz [Opens in a new window]  and
Nayereh Bagheri Khoolenjani
Fatemeh Asgari
Affiliation:
Department of Statistics,University of Isfahan, Isfahan, Iran E-mail: [email protected] and [email protected]
Mohammad Hossein Alamatsaz
Affiliation:
Department of Statistics,University of Isfahan, Isfahan, Iran E-mail: [email protected] and [email protected]
Nayereh Bagheri Khoolenjani
Affiliation:
Department of Statistics,University of Isfahan, Isfahan, Iran E-mail: [email protected] and [email protected]
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Abstract

Considering the Gaussian noise channel, Costa [4] investigated the concavity of the entropy power when the input signal and noise components are independent. His argument was connected to the first-order derivative of the Fisher information. In real situations, however, the noise can be highly dependent on the main signal. In this paper, we suppose that the input signal and noise variables are dependent. Then, some well-known copula functions are used to define their dependence structure. The first- and second-order derivatives of Fisher information of the model are obtained. Then, by using these derivatives, we will generalize two inequalities based on the Fisher information and a functional that is closely associated to Fisher information for the case when the input signal and noise variables are dependent. We will also show that the previous results for the independent case are recovered as special cases of our result. Several applications are provided to support the usefulness of our results. Finally, the channel capacity of the Gaussian noise channel model with dependent signal and noise is studied.

Keywords

Archimedean copulaCosta's EPIentropy power inequality (EPI)Farlie-Gumbel-Morgenstern (FGM) copulaGaussian copulaPrimary 94A2097K5054C7062H20
Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 33 , Issue 4 , October 2019 , pp. 618 - 657
Copyright
Copyright © Cambridge University Press 2019 

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