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A Novel Bayesian Modulation Classification Algorithm |
Liu Zheng; Wang Ming-yang; Jiang Wen-li; Zhou Yi-yu |
School of Electronic Science & Engineering, National University of Defence Technology, Changsha 410073, China |
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Abstract 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.
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Received: 15 November 2004
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