Modulation Classification of MQAM Signals from Their Constellation Using Clustering
ICCSN '10 Proceedings of the 2010 Second International Conference on Communication Software and Networks
Wireless Personal Communications: An International Journal
Relational generalizations of cluster validity indices
IEEE Transactions on Fuzzy Systems
Likelihood-Ratio Approaches to Automatic Modulation Classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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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.