Automatic Digital Modulation Recognition Based on Support Vector Machines

This paper presents a method based on support vector machines (SVMs) for recognizing digital modulation signals in the presence of additive white Gaussian noise. As a powerful method for pattern recognition, SVMs with radial basis function (RBF) kernels are incorporated to form the multi-class recognition system which employs the conventional features of each signal obtained from its amplitude, frequency, and phase information. Computer simulations of different types of band-limited digitally modulated signals corrupted by Gaussian white noise have been carried out to measure the performance of the classification method. The simulation results that the accuracy rate of this method is at lest 85.67% show that the recognition method based on SVMs is effective. And the performance of the automatic recognition method is very satisfactory with high overall success rates even in a low signal to noise ratio (SNR) environment.