Classification of digital modulations by MCMC sampling
This paper addresses the problem of classification of digital modulations. The proposed solution uses the Bayes classifier, which is implemented by the Markov chain Monte Carlo scheme. The implementation considers classifications in the presence of phase and frequency offsets as well as residual filtering effects coming from imperfect channel equalization. The proposed approach has been tested for many scenarios and its performance has been compared with the maximum likelihood classifier and the 4/sup th/ order cumulant-based method. The obtained results show that our classifier outperforms the other methods considerably.