基于循环自相关的OFDM调制识别方法  

Recognition of OFDM Signals Based on Cyclic Autocorrelation

作　　者：王玉娥[1] 张天骐[1] 白娟[1] 包锐[1] 

机构地区：[1]重庆邮电大学信号与信息处理重庆市重点实验室,重庆400065

出　　处：《电视技术》2012年第5期44-48,共5页Video Engineering

基　　金：国家自然科学基金项目(61071196);国家自然科学基金-中物院NSAF联合基金项目(10776040);教育部新世纪优秀人才支持计划项目(NCET-10-0927);信号与信息处理重庆市市级重点实验室建设项目(CSTC;2009CA2003);重庆市自然科学基金项目(CSTC;2009BB2287;CSTC;2010BB2398;CSTC;2010BB2411)

摘　　要：针对通信信号的调制识别问题,首先根据通信信号的循环平稳性,提出一种基于循环自相关的OFDM信号和单载波信号的调制识别算法,然后将小波多分辨分析理论与调制信号的瞬时特征以及高阶累积量相结合,提出一种基于小波分解的单载波信号识别方法,在此基础上采用分层结构的神经网络分类器对OFDM,2ASK,4ASK,2PSK,4PSK,8PSK,16QAM这7种调制信号进行识别。仿真结果表明该方法具有良好的分类性能,且对噪声不敏感。For the problem of modulation recognition of communication signals, an algorithm based on cyclic autocorrelation is proposed to recognize OFDM signals and single-carrier signals according to the cycle-stationarity of communication signals. Then a single-cartier signals recognition method is proposed based on wavelet decomposition whicb combines wavelet theory of muhiresolution analysis with modulated signals＇ instantaneous characteristics and high-order cumulants. What＇ s more, a hierarchical neural network classifier is used to identify seven kinds of modulation signals as OFDM, 2ASK, 4ASK, 2PSK, 4PSK, 8PSK, 16QAM. The simulation results show that the method is high in performance and not sensitive to noise.

关 键 词：OFDM 调制识别 小波 瞬时特征 高阶累积量 神经网络 

分 类 号：TN911[电子电信—通信与信息系统]