Abstract: A fast convergence Bayesian estimator of the class A model parameters is derived and calculated using the Markov Chain Monte Carlo (MCMC) procedure. This estimator can estimate the impulsive index, Gauss-to-impulsive power ratio, noise power, and hidden states of class A noise model for the channel simultaneously. The considered estimator is different from traditional estimator, which provides a novel method with low-complexity, global optimization capability and potential for parallel processing. Simulation with small sample sizes shows effectiveness of the technique.

Key words: non-Gaussian noise, class A noise, impulsive noise