Sphere decoding inspired approximation method to compute the entropy of large Gaussian mixture distributions

The computation of mutual informations of large scale systems with finite input alphabet and Gaussian noise has often prohibitive complexities. In this paper, we propose a novel approach exploiting the sphere decoding concept to bound and approximate such mutual information term with reduced complex...

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Bibliographic Details
Published in2014 IEEE Workshop on Statistical Signal Processing (SSP) pp. 264 - 267
Main Authors Su Min Kim, Tan Tai Do, Oechtering, Tobias J., Peters, Gunnar
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2014
Subjects
Approximation method
Approximation methods
Complexity theory
Decoding
Finite input alphabet
Gaussian mixture distribution
Mutual information
Signal to noise ratio
Sphere decoding
Time-frequency analysis
Vectors
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Summary:The computation of mutual informations of large scale systems with finite input alphabet and Gaussian noise has often prohibitive complexities. In this paper, we propose a novel approach exploiting the sphere decoding concept to bound and approximate such mutual information term with reduced complexity and good accuracy. Using Monte-Carlo simulations, the method is numerically demonstrated for the computation of the mutual information of a frequency- and time-selective channel with QAM modulation.
ISSN:2373-0803
2693-3551
DOI:10.1109/SSP.2014.6884626