Comparison of clustering algorithms for analog modulation classification

This study introduces a comparative study of implementation of clustering algorithms on classification of the analog modulated communication signals. A number of key features are used for characterizing the analog modulation types. Four different clustering algorithms are used for classifying the analog signals. These most representative clustering techniques are K-means clustering, fuzzy C-means clustering, mountain clustering and subtractive clustering. Performance comparison of these clustering algorithms and the advantages and disadvantages of the methods are examined. The validity analysis is performed. The study is supported with computer simulations.