Automatic Modulation Classification plays a significant role in Cognitive Radio to identify the modulation format of the primary user. In this paper, we present the Stockwell transform (S-transform) based features extraction for classification of different digital modulation schemes using different classifiers such as Neural Network (NN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Naive Bayes (NB), k-Nearest Neighbor (k-NN). The S – transform provides time-frequency or spatial-frequency localization of a signal. This property of S-transform gives good discriminant features for different modulation schemes.
Two simple features i.e., energy and entropy are used for classification. Different modulation schemes i.e., BPSK, QPSK, FSK and MSK are used for classification. The results are compared with wavelet transform based features using probability of correct classification, performance matrix including classification accuracy and computational complexity (time) for SNR range varying from 0 to 20 dB. Based upon the results, we found that S-transform based features outperform wavelet transform based features with better classification accuracy and less computational complexity.