Sphere decoding inspired approximation method to compute the entropy of large Gaussian mixture distributions
2014 (English)In: IEEE Workshop on Statistical Signal Processing Proceedings, 2014, 264-267 p.Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
2014. 264-267 p.
Approximation method, Finite input alphabet, Gaussian mixture distribution, Mutual information, Sphere decoding, Approximation theory, Decoding, Gaussian noise (electronic), Intelligent systems, Signal processing, Approximation methods, Mutual informations, Stereo vision
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:kth:diva-167952DOI: 10.1109/SSP.2014.6884626ISI: 000361019700067ScopusID: 2-s2.0-84907396576ISBN: 9781479949755OAI: oai:DiVA.org:kth-167952DiVA: diva2:817177
2014 IEEE Workshop on Statistical Signal Processing, SSP 2014, 29 June 2014 through 2 July 2014, Gold Coast, QLD
QC 201506042015-06-042015-05-222015-10-08Bibliographically approved