Modulation Classification via Gibbs Sampling Based on a Latent Dirichlet   Bayesian Network
  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. 
Yu Liu, Osvaldo Simeone, Alexander M. Haimovich and Wei Su
cs.IT cs.CV math.IT