Hierarchical digital modulation recognition using support vector machines

Most current digital modulation recognition methods are limited by the computational complexity and the training of the classifiers.A hierarchical method for multi-class digital modulation recognition using support vector machines(SVM) was developed as a simplified method.The method uses cumulants and simultaneous frequency statistical moments of the received signals as the features and support vector machines as the classifiers.This hierarchical method is less complex computationally and has faster classifier training speed compared with other methods.Moreover,the method is robust in the presence of carrier phase and frequency offsets with Gaussian noise.Classification results for 11 modulation types including ASK,FSK,PSK,and QAM obtained from computer simulations,show that the overall success rate is 95% with Gaussian noise having SNR ≥5 dB.