Automatic modulation classification using different neural network and PCA combinations

Abstract

Abstract This paper highlights one of the most promising research directions for automatic modulation recognition algorithms, although it does not provide a final solution. We study the design of a high-precision classifier for recognizing PSK, QAM and DVB-S2 APSK modulation signals. First, an efficient pattern recognition model that includes three main modules for feature extraction, feature optimization and classification is presented. The feature extraction module extracts the most useful combinations of up to six high-order cumulants that embed sixth-order moments and uses logarithmic function properties to improve the distribution curve of the six-order cumulants. To the best of our knowledge, this is the first time that these combinations and the improved feature criteria have been applied in this area. The optimizer module selects optimal features via principal component analysis (PCA). Then, in the classifier module, we study two important supervised neural network classifiers (i.e., multilayer perceptron (MLP)- and radial basis function (RBF)-based classifiers). Through an experiment, we determine the best classifier for recognizing the considered modulations. Then, we propose an RBF-PCA combined recognition system in which an optimization module is added to enhance the overall classifier performance. This module optimizes the classifier performance by searching for the best subset of features to use as the classifier input. The simulation results illustrate that the RBF-PCA classifier combination achieves high recognition accuracy even at a low signal-to-noise ratio (SNR) and with limited training samples.