[1] Chen Liang, Cheng Hanwen, Wu Lenan, et al. Modulation classification of MPSK signalsbased on nonparametric Bayesian inference [J]. Journal of Southeast University (English Edition), 2009, 25 (2): 171-174. [doi:10.3969/j.issn.1003-7985.2009.02.006]

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Modulation classification of MPSK signalsbased on nonparametric Bayesian inference()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

25

Issue:

2009 2

Page:

171-174

Research Field:

Information and Communication Engineering

Publishing date:

2009-06-30

Info

Title:

Modulation classification of MPSK signalsbased on nonparametric Bayesian inference

Author(s):

Chen Liang¹;  2;  Cheng Hanwen¹;  Wu Lenan¹

¹School of Information Science and Engineering, Southeast University, Nanjing 210096, China
² School of Mechanical Engineering, Jiangsu University, Zhenjiang 212013, China

Keywords:

modulation classification;  M-ary phase shift keying;  Dirichlet process;  nonparametric Bayesian inference;  Monte Carlo Markov chain

PACS:

TN911

DOI:

10.3969/j.issn.1003-7985.2009.02.006

Abstract:

A nonparametric Bayesian method is presented to classify the MPSK(M-ary phase shift keying)signals.The MPSK signals with unknown signal noise ratios(SNRs)are modeled as a Gaussian mixture model with unknown means and covariances in the constellation plane, and a clustering method is proposed to estimate the probability density of the MPSK signals.The method is based on the nonparametric Bayesian inference, which introduces the Dirichlet process as the prior probability of the mixture coefficient, and applies a normal inverse Wishart(NIW)distribution as the prior probability of the unknown mean and covariance.Then, according to the received signals, the parameters are adjusted by the Monte Carlo Markov chain(MCMC)random sampling algorithm.By iterations, the density estimation of the MPSK signals can be estimated.Simulation results show that the correct recognition ratio of 2/4/8PSK is greater than 95% under the condition that SNR>5 dB and 1 600 symbols are used in this method.

References:

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[12] Neal R M.Markov chain sampling methods for Dirichlet process mixture models [J].Journal of Computational and Graphical Statistics, 2000, 9(2):249-265.
[13] Ruo Ming, Yang Shaoquan.Modulation classification of MPSK signals based on dynamic clustering [J].Journal of Circuit and Systems, 2005, 10(2):83-87.(in Chinese)

Memo

Memo:

Biographies: Chen Liang(1977—), male, graduate;Wu Lenan(corresponding author), male, doctor, professor, wuln@seu.edu.cn.
Foundation item: Cultivation Fund of the Key Scientific and Technical Innovation Project of Ministry of Education of China(No.3104001014).
Citation: Chen Liang, Cheng Hanwen, Wu Lenan.Modulation classification of MPSK signals based on nonparametric Bayesian inference[J].Journal of Southeast University(English Edition), 2009, 25(2):171-174.

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