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In this study, an algorithm to blind and automatic modulation classificationhas been proposed. It well benefits combined machine leaning and signal featureextraction to recognize diverse range of modulation in low signal power tonoise ratio (SNR). The presented algorithm contains four. First, it advantagesspectrum analyzing to branching modulated signal based on regular and irregularspectrum character. Seconds, a nonlinear soft margin support vector (NS SVM)problem is applied to received signal, and its symbols are classified tocorrect and incorrect (support vectors) symbols. The NS SVM employment leads todiscounting in physical layer noise effect on modulated signal. After that, ak-center clustering can find center of each class. finally, in correlationfunction estimation of scatter diagram is correlated with pre-saved idealscatter diagram of modulations. The correlation outcome is classificationresult. For more evaluation, success rate, performance, and complexity incompare to many published methods are provided. The simulation prove that theproposed algorithm can classified the modulated signal in less SNR. Forexample, it can recognize 4-QAM in SNR=-4.2 dB, and 4-FSK in SNR=2.1 dB with%99 success rate. Moreover, due to using of kernel function in dual problem ofNS SVM and feature base function, the proposed algorithm has low complexity andsimple implementation in practical issues.

Blind Modulation Classification via Combined Machine Learning and Signal Feature Extraction