Modulation Classification Using Spectral Features on Fading Channels

Modulation classification plays a key role in demodulation of the received signal for extracting the required information. Modulation classification is a difficult task when there is no information about the received signal (Blind Classification) and especially in presence of multipath fading and white guassian noise. Emerging applications of automatic modulation classification (AMC) in military, civil and cognitive radio (CR) applications leads to the development of various AMC algorithms. The Automatic modulation classification can be achieved by two major approaches, Likelihood based and features based pattern classification approach. In this paper first we have analyze and discourse the merits and demerits of both categories, then we have proposed an algorithm based on spectral features of the modulated signal. The proposed classifier is Multilayer perceptron which is also referred as feed forward back propagation neural network. The channels considered throughout the simulations are additive white guassian noise (AWGN) channel, Rayleigh flat fading and Rician flat fading channel. The considered modulation formats are PAM 2 to 64, PSK 2 to 64, FSK 2 to 64 and QAM 2 to 64. The proposed algorithm will recognize the considered modulation formats with 100% success at 0dB SNR. Tables in the form of confusion matrix and graphs shows correct classification rate for considered modulation formats.