[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()

基于非参数贝叶斯推断的MPSK信号调制识别

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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

基于非参数贝叶斯推断的MPSK信号调制识别

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

陈亮¹;  2;  程汉文¹;  吴乐南¹

¹东南大学信息科学与工程学院, 南京 210096; ²江苏大学机械工程学院, 镇江 212013

Keywords:

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

调制分类;  多元相移键控;  Dirichlet过程;  非参数贝叶斯推断;  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.

依据星座图采用非参数贝叶斯方法对多元相移键控(MPSK)信号进行调制识别.将未知信噪比(SNR)水平的MPSK信号看成复平面内多个未知均值和方差的高斯分布依照一定的比例混合而成, 利用非参数贝叶斯推断方法进行密度估计, 实现对MPSK信号分类目的.推断过程中, 引入Dirichlet 过程作为混合比例因子的先验分布, 结合正态逆Wishart(NIW)分布作为均值和方差的先验分布, 根据接收信号, 利用Gibbs采样的MCMC(Monte Carlo Markov chain)随机采样算法, 不断调整混合比例因子、均值和方差.通过多次迭代, 得到对调制信号的密度估计.仿真表明, 在SNR>5 dB, 码元数目大于1 600时, 2/4/8PSK的识别率超过了95%.

References:

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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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