Title: Combining clustering and SVM for automatic modulation classification

Authors: Aisheng Liu; Qi Zhu

Addresses: Jiangsu Key Laboratory of Wireless Communications, Nanjing University of Posts and Telecommunications, Nanjing City, Jiangsu Province 210003, China; Key Laboratory on Wideband Wireless Communications and Sensor Network Technology of Ministry of Education, Nanjing University of Posts and Telecommunications, Nanjing City, Jiangsu Province 210003, China. ' Jiangsu Key Laboratory of Wireless Communications, Nanjing University of Posts and Telecommunications, Nanjing City, Jiangsu Province 210003, China; Key Laboratory on Wideband Wireless Communications and Sensor Network Technology of Ministry of Education, Nanjing University of Posts and Telecommunications, Nanjing City, Jiangsu Province 210003, China

Abstract: In this paper, we propose a new modulation classification method based on the combination of clustering and Support Vector Machine (SVM), in which a new algorithm is introduced to extract key features. To recognise signals modulated based on constellation diagram, such as MPSK and MQAM; K-means clustering is adopted for recovering constellation under different number of clusters. Silhouette index is employed as a cluster validity measure to extract key features that discriminate between different modulation types. Then hierarchical SVM classifier is designed to recognise modulation types according to the key features extracted. Simulation results show that the classification rates of the algorithm proposed in this paper are much higher than those of clustering algorithm.

Keywords: AMC; automatic modulation classification; K-means clustering; silhouette index; SVM; support vector machines; feature extraction; simulation; constellation diagram.

DOI: 10.1504/IJCAT.2012.051124

International Journal of Computer Applications in Technology, 2012 Vol.45 No.4, pp.245 - 253

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 18 Dec 2012 *

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