Modulation Identification of Satellite Signals Using an Intelligent System

Automatic signal type identifier plays an important role for modulation identification of satellite signals. Most of the proposed methods didn’t have good performance in low level of signal to noise ratios (SNRs). Also they cant identify more digital modulations. This study investigates the design of an accurate system for identification of digital modulations. First, it is introduced an efficient system 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. In the classifier module, an efficient supervised classifier, i.e. radial basis function neural network is proposed. The results show this system has good performance and recognize a lot of digital modulations. However the performance of system degrades at very low SNRs. Also selection of the parameters of the classifier and feature selection is made by trial and error method. The tradeoff between them is a difficult problem. Then at the second fold we have proposed a hybrid intelligent system which an optimization module, i.e. bees algorithm (BA) is considered in the previous system. This module optimizes the classifier design by searching for the best value of the parameters and the best subset of features that feed the classifier. Simulation results show that the proposed hybrid intelligent system has very high identification accuracy even at very low SNRs. This high efficiency is achieved with little features, which have been selected using BA.