Blind digital modulation classification in software radio using the optimized classifier and feature subset selection

Automatic recognition of digital modulations plays an important role in various applications such as software defined radio. This study investigates the design of an accurate system for recognition of digital modulations. First, an efficient system is introduced that includes two main modules: the feature extraction module and the classifier module. First module extracts a suitable combination of the higher order moments up to eighth, higher order cumulants up to eighth and instantaneous characteristics of digital modulations. These features are applied for the first time in this area. In the classifier module, several supervised classifiers, such as multilayer perceptron neural network, radial basis function and multi-class support vector machine based classifier are investigated. By experimental study, we choose the best classifier for recognition of the considered modulations. Then, we propose a hybrid heuristic recognition system to which an optimization module is added to improve the generalization performance of the classifier. This module optimizes the classifier design by searching for the best value of the parameters that tune its discriminant function (kernel parameters selection) and upstream by looking for the best subset of features that feed the classifier. Simulation results show that the proposed system has a very high recognition accuracy. This high efficiency is achieved with little features, which have been selected using particle swarm optimizer.