Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Digital modulation classification using constellation shape
Signal Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic digital modulation recognition using artificial neural network and genetic algorithm
Signal Processing - Special issue on independent components analysis and beyond
A Blind Modulation Type Detector for DPRS Standard
Wireless Personal Communications: An International Journal
Expert Systems with Applications: An International Journal
Intelligent digital signal-type identification
Engineering Applications of Artificial Intelligence
Principal Component Analysis Based on L1-Norm Maximization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Signal Processing
Automatic digital modulation recognition based on ART2A-DWNN
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
An iterative pruning algorithm for feedforward neural networks
IEEE Transactions on Neural Networks
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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 themodulation order without any priori signal information. Pre-processing and features subset selection using principal component analysis is used to reduce the network complexity and to improve the classifier's performance. The proposed algorithm is evaluated through confusion matrix and false recognition probability. The proposed classifier is shown to be capable of recognizing the modulation scheme with high accuracy over wide signal-to-noise ratio (SNR) range over both additive white Gaussian noise (AWGN) and different fading channels.