Modulation Classification via Gibbs Sampling Based on a Latent Dirichlet Bayesian Network
Information Theory
2016-11-15 v2 Computer Vision and Pattern Recognition
math.IT
Abstract
A novel Bayesian modulation classification scheme is proposed for a single-antenna system over frequency-selective fading channels. The method is based on Gibbs sampling as applied to a latent Dirichlet Bayesian network (BN). The use of the proposed latent Dirichlet BN provides a systematic solution to the convergence problem encountered by the conventional Gibbs sampling approach for modulation classification. The method generalizes, and is shown to improve upon, the state of the art.
Keywords
Cite
@article{arxiv.1408.0765,
title = {Modulation Classification via Gibbs Sampling Based on a Latent Dirichlet Bayesian Network},
author = {Yu Liu and Osvaldo Simeone and Alexander M. Haimovich and Wei Su},
journal= {arXiv preprint arXiv:1408.0765},
year = {2016}
}
Comments
Contains corrections with respect to the version to appear on IEEE Signal Processing Letters (see Fig. 2)