A Novel Bayesian Modulation Classification Algorithm
一种新的贝叶斯调制分类算法

A novel method is proposed for digital modulation classification based on Markov chain Monte Carlo (MCMC). Considering the difficulty for Bayesian classifier with unknown residual carrier phase and frequency, marginal likelihood probability density is estimated by Metropolis-Hastings (M-H) algorithm, which kept the theoretical optimality and robustness of Bayesian classifier. The simulated results show that the novel classifier outperforms the one based on cumulants.