Automatic modulation classification based on the combination of clustering and neural network

作     者：LIUAi-sheng ZHU Qi 

作者机构：Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Nanjing 210003 China Key Laboratory on Wideband Wireless Communications and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing 210003 China 

出 版 物：《The Journal of China Universities of Posts and Telecommunications》 (中国邮电高校学报（英文版）)

年 卷 期：2011年第18卷第4期

页      面：13-19,38页

核心收录：

学科分类：11[军事学] 0810[工学-信息与通信工程] 1105[军事学-军队指挥学] 07[理学] 081104[工学-模式识别与智能系统] 08[工学] 070104[理学-应用数学] 081002[工学-信号与信息处理] 110503[军事学-军事通信学] 0701[理学-数学] 0811[工学-控制科学与工程] 

基　　金：supported by the National Basic Research Program of China (2007CB310607) the National Natural Science Foundation of China (60772062) the National Science and Technology Key Project (2011ZX03001-006-02,2011ZX03005-004-03) 

主　　题：modulation classification clustering BP neural network FCM 

摘      要：In this paper, we propose a new modulation classification method based on the combination of clustering and neural network, in which a new algorithm is introduced to extract key features. In order to recognize modulation types based on the constellation diagram such as phase shift keying （PSK） and quadrature amplitude modulation （QAM）, fuzzy C-means （FCM） clustering is adopted for recovering the constellation under different number of clusters. Then cluster validity measure is applied to extract key features which discriminate between different modulation types. The features are sent to neural network so that modulation types can be recognized. In order to conquer the disadvantages of standard back propagation （BP） neural network, conjugate gradient learning algorithm of Polak-Ribiere update is employed to improve the speed of convergence and the performance of modulation recognition. Simulation results show that classification rates of the algorithm proposed in this paper are much higher than those of clustering algorithm.