A Novel Approach To Mpsk Modulation Recognition Through Compressive Signal Processing Strategy

The theory of compressive sensing (CS) enables the recovery of sparse or approximately sparse signal from a significantly small set of measurements. This progress has inspired a new idea that many signal processing problems can be solved based on the framework of CS. In this work, we employ CS in the task of inferring the modulation of a communication signal without full signal recovery which is the usual focus in previous literatures. We show that the differences in the higher order power spectrum between MPSK signals can be used as the features for automatic modulation recognition (AMR). Subsequently, we present the sparsity of these features in the Fourier domain and provide an approach to extract features from nonuniform compressive samples of observed signals. The proposed method is validated by simulation experiments through the support vector machine (SVM).