Automatic Modulation Classification Using Cyclic Correntropy Spectrum in Impulsive Noise

Automatic modulation classification (AMC) plays an important role in many military and civilian communication applications. However, it remains a challenging task to support such AMC mechanisms under impulsive noise environments. Aiming at improving the classification performance in impulsive noise, in this letter, a novel modulation classification method is proposed by using the cyclic correntropy spectrum (CCES). In the proposed method, CCES is introduced into AMC for effectively suppressing impulsive noise. Specifically, it is verified that modulation types can be distinguished through CCES. Then, multi-slices are extracted at different cycle-frequencies from CCES as the original features for AMC. Following the extraction, the principal component analysis is applied to these slices to further optimize the original features. Finally, the radial basis function neural network is used as a classifier to perform modulation classification. Monte Carlo simulations demonstrate that the proposed algorithm outperforms other existing schemes in impulsive noise cases, especially with a low generalized signal to noise ratio.