Modulation type is one of the most important characteristics used in signal waveform
identification. In this paper, an algorithm for automatic digital modulation recognition is
proposed. The proposed algorithm is verified using higher-order statistical moments (HOM)
of continuous wavelet transform (CWT) as a features set. A multilayer feed-forward neural
network trained with resilient backpropagation learning algorithm is proposed as a classifier.
The purpose is to discriminate among different M-ary shift keying modulation schemes and …

Modulation type is one of the most important characteristics used in signal waveform
identification. An algorithm for automatic modulation recognition has been developed and
presented in this study. The suggested algorithm is verified using higher order statistical
moments of wavelet transform as a features set. A multi-layer neural network with resilient
backpropagation learning algorithm is proposed as a classifier. The purpose is to
discriminate different M-ary shift keying modulation types and modulation order without any …