Modulation classification of MPSK signals based on nonparametric Bayesian inference  

基于非参数贝叶斯推断的MPSK信号调制识别(英文)

作　　者：陈亮[1,2] 程汉文[1] 吴乐南[1] 

机构地区：[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

出　　处：《Journal of Southeast University(English Edition)》2009年第2期171-174,共4页东南大学学报（英文版）

基　　金：Cultivation Fund of the Key Scientific and Technical Innovation Project of Ministry of Education of China(No.3104001014)

摘　　要：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>5dB,码元数目大于1600时,2/4/8PSK的识别率超过了95%.

关 键 词：modulation classification M-ary phase shift keying Dirichlet process nonparametric Bayesian inference Monte Carlo Markov chain 

分 类 号：TN911[电子电信—通信与信息系统]