Effects of symbol rate on the classification of digital modulation signals

This 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 effects of this dependence the paper study the performance of the newly proposed classifiers as well as the maximum likelihood (ML) classifier [1,2], the qLLR classifier [3], and the cumulants based classifier [4]. 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.