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Volume 28 Issue 7
Jan.  2011
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Liu Zheng, Wang Ming-yang, Jiang Wen-li, Zhou Yi-yu. A Novel Bayesian Modulation Classification Algorithm[J]. Journal of Electronics & Information Technology, 2006, 28(7): 1233-1237.
Citation: Liu Zheng, Wang Ming-yang, Jiang Wen-li, Zhou Yi-yu. A Novel Bayesian Modulation Classification Algorithm[J]. Journal of Electronics & Information Technology, 2006, 28(7): 1233-1237.

A Novel Bayesian Modulation Classification Algorithm

  • Received Date: 2004-11-15
  • Rev Recd Date: 2005-04-05
  • Publish Date: 2006-07-19
  • 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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  • Wei W, Mendel J M. Maximum-likelihood classification for digital amplitude-phase modulation. IEEE Trans. on Comm., 2000 48(2): 189-193.[2]Polydoros A, Kim K. On the detection and classification of quadrature digital modulations in broad-band noise[J].IEEE Trans. on Comm.1990, 38(8):1199-1211[3]Huang C Y, Polydoros A. Likelihood methods for MPSK modulation classification[J].IEEE Trans. on Comm.1995, 43(2/3/4):1493-[4]Azzouz E E, Nandi A K. Automatic identification of digital modulation types. SignalProcessing, 1995, 47(1): 55-69.[5]Swami A, Sadler B M. Hierarchical digital modulation classification using cumulants[J].IEEE Trans. on Comm.2000, 48(3):416-429[6]Andrieu C, De Freitas N, Doucet A, Jordan M I. An introductionto MCMC for machine learning[J].Machine Learning.2003,50(1/2):5-43[7]Chib S, Creenberg E, Understanding the Metropolis-Hastingsalgorithm. American Statistician, 1995, 49(4): 327-335.[8]Lesage S, Tourneret J Y, Djuric P M. Classification of digitalmodulations by MCMC sampling, IEEE ICASSP, Salt Lake City,Utah, May 2001, vo1.4: 2553-2556.[9]Sills J A. Maximum-likelihood modulation classification forPSK/QAM, IEEE MILCOM, Atlantic City, NJ, Oct.31-Nov.3,1999: 217-220.[10]Chib S, Jeliazkov I. Marginal likelihood from theMetropolis-Hastings output. Journal of the American StatisticalAssociation, 2001, 96(453): 270-281.[11]Spall J C. Estimation via Markov chain Monte Carlo[J].IEELControl Systems Magazine.2003, 23(2):34-45
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