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Modulation Classification via Gibbs Sampling Based on a Latent Dirichlet Bayesian Network (1408.0765v2)
Published 4 Aug 2014 in cs.IT, cs.CV, and 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.