-PAM Signals Classification Using Modified Gabor Filter Network

A Modified Gabor Filter (MGF) network based approach is used for feature extraction and classification of -ary Pulse Amplitude Modulated ( -PAM) signals by adaptively tuning the parameters of MGF network. Modulation classification of -PAM signals is done under the influence of additive white Gaussian noise (AWGN) and channel effects such as Rayleigh flat fading and Rician flat fading. The MGF network uses the network structure of two layers. First layer which is input layer constitutes the adaptive feature extraction part and second layer constitutes the signal classification part. The Gabor atom parameters are tuned using Delta rule and updating of weights of MGF using Recursive Least Square (RLS) algorithm. The simulation results in the form confusion matrix show that proposed modified modulation classification algorithm has high classification accuracy at low signal to noise ratio (SNR). The performance comparison with state-of-the-art existing techniques shows the significant performance improvement of proposed MGF based classifier.