Automatic Recognition of Digital Communication Signal

Automatic recognition of digital communication signals has seen increasing demands nowadays in various applications. This paper investigates the design of high efficient system for classification of the digital communication signals. The system includes two main modules: feature extraction and classification. In the feature extraction module we have used a novel balanced combination of the higher order moments (up to eighth), higher order cumulants (up to eighth) and spectral characteristics. In the classifier we have investigated the performances of the radial basis function (RBF) neural network, probability neural network (PNN) and multilayer perceptron (MLP) neural network. Then we have compared these systems. Experimental results show the proposed systems have high percentage of correct classification to discriminate the different types of digital signals even at very low SNRs.