Automatic Modulation Recognition of Communication Signals
Automatic Modulation Recognition of Communication Signals
Digital Modulation identification model using wavelet transform and statistical parameters
Journal of Computer Systems, Networks, and Communications
Communication Systems
Higher-order cyclic cumulants for high order modulation classification
MILCOM'03 Proceedings of the 2003 IEEE conference on Military communications - Volume I
Modulation Recognition of MFSK Signals Based on Multifractal Spectrum
Wireless Personal Communications: An International Journal
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This paper proposes a neural network (NN) based intelligent decision making system for digital modulation classification using wavelet transform, histogram peak and higher order statistical moments. The decision making system is developed to classify the modulation schemes buried in additive white Gaussian noise and channel interference utilizing NN classifier. The performance is verified and validated for M-ary PSK, M-ary FSK, M-ary QAM and GMSK modulation schemes by examining the receiver operating characteristics, confusion matrix and probability of correct identification for various signal-to-noise ratios (SNR) and also for various decision parameters. The performance of the proposed system also has been compared with existing methods and found that this method can be considered as reliable classification method for Digital Modulation Scheme with lower SNR upto 驴 5 dB.