  In this study, an algorithm to blind and automatic modulation classification
has been proposed. It well benefits combined machine leaning and signal feature
extraction to recognize diverse range of modulation in low signal power to
noise ratio (SNR). The presented algorithm contains four. First, it advantages
spectrum analyzing to branching modulated signal based on regular and irregular
spectrum character. Seconds, a nonlinear soft margin support vector (NS SVM)
problem is applied to received signal, and its symbols are classified to
correct and incorrect (support vectors) symbols. The NS SVM employment leads to
discounting in physical layer noise effect on modulated signal. After that, a
k-center clustering can find center of each class. finally, in correlation
function estimation of scatter diagram is correlated with pre-saved ideal
scatter diagram of modulations. The correlation outcome is classification
result. For more evaluation, success rate, performance, and complexity in
compare to many published methods are provided. The simulation prove that the
proposed algorithm can classified the modulated signal in less SNR. For
example, 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 of
NS SVM and feature base function, the proposed algorithm has low complexity and
simple implementation in practical issues.
