An overview of feature-based methods for digital modulation classification

This paper presents an overview of feature-based (FB) methods developed for Automatic classification of digital modulations. Only the most well-known features and classifiers are considered, categorized, and defined. The features include instantaneous time domain (ITD) parameters, Fourier transform (FT), wavelet transform (WT), higher order moments (HOM) to name a few. The classifiers are artificial neural networks (ANN), support vector machines (SVMs), and decision tree (DT). We also highlight the advantages and disadvantages of each technique in classifying a certain modulation scheme. The objective of this work is to assist newcomers to the field to choose suitable algorithms for intended applications. Furthermore, this work is expected to help in determining the limitations associated with the available FB automatic modulation classification (AMC) methods.