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On the Entropy Computation of Large Complex Gaussian Mixture Distributions
KTH, School of Electrical Engineering (EES), Signal Processing.
KTH, School of Electrical Engineering (EES), Communication Theory.ORCID iD: 0000-0002-0036-9049
2015 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 63, no 17, 4710-4723 p.Article in journal (Refereed) Published
Abstract [en]

The entropy computation of Gaussian mixture distributions with a large number of components has a prohibitive computational complexity. In this paper, we propose a novel approach exploiting the sphere decoding concept to bound and approximate such entropy terms with reduced complexity and good accuracy. Moreover, we propose an SNR region-based enhancement of the approximation method to reduce the complexity even further. Using Monte-Carlo simulations, the proposed methods are numerically demonstrated for the computation of the mutual information including the entropy term of various channels with finite constellation modulations such as binary and quadratic amplitude modulation (QAM) inputs for communication applications.

Place, publisher, year, edition, pages
2015. Vol. 63, no 17, 4710-4723 p.
Keyword [en]
Entropy approximation, finite input alphabet, Gaussian mixture distribution, mutual information, sphere decoding
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-173422DOI: 10.1109/TSP.2015.2441046ISI: 000360064700021Scopus ID: 2-s2.0-84939240423OAI: oai:DiVA.org:kth-173422DiVA: diva2:855122
Note

QC 20150918

Available from: 2015-09-18 Created: 2015-09-11 Last updated: 2017-12-04Bibliographically approved

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Oechtering, Tobias J.

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