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

作　　者：LIUAi-sheng ZHU Qi 

机构地区：[1]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　　处：《The Journal of China Universities of Posts and Telecommunications》2011年第4期13-19,38,共8页中国邮电高校学报（英文版）

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)

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.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.

关 键 词：modulation classification CLUSTERING BP neural network FCM 

分 类 号：TN911.3[电子电信—通信与信息系统] O235[电子电信—信息与通信工程]