A Novel Method of Modulation Classification for Digital Signals

This paper introduces a novel method of modulation classification for digitally modulated signals in the presence of additive white Gaussian noise (AWGN). This method does not need any prior knowledge of the signals, such as SNR (Signal-to-Noise Ratio), symbol rate and carrier frequency. Four kinds of features are extracted to achieve a tree-based classification approach, and three radial basis function (RBF) neural networks are employed in the classifier. The computer simulation is carried out and the results are presented.