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

doi:10.1016/S1005-8885(10)60077-5

Acta Metallurgica Sinica(English letters) ›› 2011, Vol. 18 ›› Issue (4): 13-19.doi: 10.1016/S1005-8885(10)60077-5

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

刘爱声,朱琦

南京邮电大学

收稿日期:2010-12-15 修回日期:2011-04-07 出版日期:2011-08-31 发布日期:2011-08-24
通讯作者: 刘爱声 E-mail:yuerushui0634@126.com
基金资助:
This work was supported by the National Basic Research Program of China (2007CB310607), the National Natural Science Foundation of China (60772062), and the National Science and Technology Key Project (2011ZX03001-006-02, 2011ZX03005-004-03).

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

Received:2010-12-15 Revised:2011-04-07 Online:2011-08-31 Published:2011-08-24
Supported by:
This work was supported by the National Basic Research Program of China (2007CB310607), the National Natural Science Foundation of China (60772062), and the National Science and Technology Key Project (2011ZX03001-006-02, 2011ZX03005-004-03).

摘要/Abstract

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.

关键词:

modulation classification, clustering, BP neural network, FCM

Abstract:

Key words:

modulation classification, clustering, BP neural network, FCM

LIU Ai-sheng, ZHU Qi. Automatic modulation classification based on the combination of clustering and neural network[J]. Acta Metallurgica Sinica(English letters), 2011, 18(4): 13-19.

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

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

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