Automatic classification of combined analog and digital modulation schemes using feedforward neural network
Jide Julius Popoola, Rex van Olst · 2011
This paper presents an artificial neural network based automatic modulation classifier system which can be used to classify combined analog and digital modulation schemes. Four best known analog modulation schemes and five corresponding digital modulation schemes were considered. An approach that involves three different steps in developing an automatic modulation classification is presented. The first step involves the extraction of the statistical feature keys used as the inputs to the classifier. The statistical feature keys are extracted from instantaneous amplitude, instantaneous frequency and phase of the simulated signals using MATLAB code. The second step involves the development of the automatic modulation classifier based on a backpropagation neural network algorithm. The third step of the methodology involves the performance evaluation of the developed automatic modulation classifier with a related study from the research literature. Results obtained show that the developed classifier is accurate and sensitive to classification of the nine modulation schemes considered with an average success rate above 99.0%.