SVM-based classification of digital modulation signals

Modulation recognition systems have to be able to correctly classify the incoming signal's modulation scheme in the presence of noise. This paper addresses the problem of automatic modulation recognition of digital communication signals using support vector machines (SVM). Three digital modulation schemes have been considered and four features have been used as inputs to the SVM. A fuzzy multi-class classification method has been proposed and the overall accuracy of 77.0% at signal-to-noise ratio (SNR) of 10dB has been achieved.