A novel approach for digital radio signal classification: Wavelet packet energy–multiclass support vector machine (WPE–MSVM)

Abstract

In this study, a novel application of wavelet packet energy-multicass support vector machine (WPE-MSVM) is proposed to perform automatic modulation classification of digital radio signals. In this approach, first, the discrete wavelet packet transforms (DWPTs) of digital modulated radio signal types are performed. Second, the wavelet packet energies of these DWPTs are calculated. Third, these wavelet packet energy features are given to inputs of multiclass support vector machine (MSVM) classifier. Fourth, test data is given to inputs of MSVM classifier for evaluating the classification performance of this proposed classification approach. Here, db2, db3, db4, db5, db8, sym2, sym3, sym5, sym7, sym8, bior1.3, bior2.2, bior2.8, coif1 and coif5 wavelet packet decomposition filters are separately used for DWPT of these digital modulated radio signals, respectively. Thus, performance comparisons of these wavelet packet decomposition filters for digital radio signal classification are performed by using wavelet packet energy features. The digital radio signal types used in this study are 9 types, which are ASK-2, ASK-4, ASK-8, FSK-2, FSK-4, FSK-8, PSK-2, PSK-4 and PSK-8. These experimental studies are realized by using total 2250 digital modulated signals for these digital radio signal types. The rate of mean correct classification is about 90% for the sample digital modulated signals.