MCMC methods based modulation classification

In this paper, we propose a modulation classifier based on Markov Chain Monte Carlo (MCMC) methods for amplitude-phase modulated signals with multiple unknown parameters such as residual channel effects and noise power. A new Bayesian classifier framework is developed, where Monte Carlo integration is used to approximate the integration of high-dimensional function in the posterior distribution. The proposed classifier is then verified via extensive simulations and comparisons with existing approaches.