Signal Classification Based on Cyclostationary Spectral Analysis and HMM/SVM in Cognitive Radio

Distinction of the type of modulated signals is very important in cognitive radio system. In this paper, a novel approach to signal classification is proposed for cognitive radio. Combining the spectral cyclostationary features, embed SVM into the framework of HMM to construct a hybrid HMM/SVM classifier for signal recognition. The simulation results show that the high performance and robustness of the proposed approach, even in low SNR of -5 dB. Compared to the conventional methods including the classifiers based on HMM or ANN, the proposed approach has a rather higher recognition rate of signals.