Modulation Classification via Gibbs Sampling Based on a Latent Dirichlet Bayesian Network
This work addresses modulation classification for wireless communication systems, representing an incremental advance with specific improvements in convergence and performance.
The paper tackles modulation classification in single-antenna systems over frequency-selective fading channels by proposing a novel Bayesian scheme based on Gibbs sampling and a latent Dirichlet Bayesian network, which improves upon the state of the art by addressing convergence issues.
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.