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Effects of symbol rate on the classification of digital modulation signals

The paper considers features based on the multiplication of two consecutive signal values. Furthermore, three new classifiers using the features are proposed: fixed threshold tree classifier, dynamic threshold tree classifier and support vector machine (SVM) classifier. It is shown that the multiplication produces dependence of the features on the symbol rate. In order to quantify the effects of this dependence, the paper studies the performance of the newly proposed classifiers as well as the maximum likelihood (ML) classifier (Wei, W., 1998; Wei and Mendel, J.M., 2000), the qLLR classifier (Polydoros, A. and Kim, K., 1990), and the cumulants based classifier (Swami, A. and Sadler, B.M., 2000). Simulations show that the SVM classifier has promising results in the sense that it is closest to the theoretically optimal results obtained by the ML classifier.