Modulation classification using cyclostationary features on fading channels

In this study Automatic Modulation Classification (AMC) which is based on cyclostationary property of the modulated signal are discussed and implemented for the purpose of classification. Modulation Classification (MC) is a technique used to make better the overall performance of cognitive radios. Recently Cognitive Radio (CR) plays a key role in the field of communication. CR also used in the development of different wireless application and the exploitation of civilian and military applications. In modulated signals there is cyclostationary property that can be used for the detection of modulation formats. The extraction of cyclostationary features, is used for classification of digital modulation schemes at different values of SNR's, the considered modulation formats are FSK [2-64], PSK [2-64], PAM [2-64] and QAM [2-64] and the channel models considered are AWGN and Rayleigh flat fading. When the receiver, receives the signal it extract the cyclostationary features i-e Spectral Coherence Function (SCF) and Cyclic Domain Profile (CDP) and then uses a multilayer perception which is also known as Feed Forward Back Propagation Neural Network (FFBPNN) for classification of the modulation formats. The performance of proposed algorithm in the form of confusion matrix shows the correct classification accuracy of the considered modulation format. The simulation result shows the performance of proposed algorithm and feature extraction at lower SNR's. © Maxwell Scientific Organization, 2014.