On the Entropy Computation of Large Complex Gaussian Mixture Distributions
Abstract
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.
Keywords
Cite
@article{arxiv.1502.03790,
title = {On the Entropy Computation of Large Complex Gaussian Mixture Distributions},
author = {Su Min Kim and Tan Tai Do and Tobias J. Oechtering and Gunnar Peters},
journal= {arXiv preprint arXiv:1502.03790},
year = {2015}
}
Comments
14 pages, Accepted to IEEE Transactions on Signal Processing