Automatic modulation classification with deep learning-based frequency selection filters

Automatic modulation classification (AMC) is an important and challenging task that aims to discriminate modulation formats of received signals, such as military communications, cognitive radio and spectrum management. With the development of deep learning techniques, research in AMC has gained promising results because of its powerful representation and classification abilities. In this Letter, the authors present a new network architecture that combines a frequency selection module and a convolutional neural network (CNN). This scheme not only processes raw signal data with carriers to increase the in-band signal-to-noise ratio but also converge faster than traditional CNN. Experiments demonstrate the effectiveness and efficiency of the proposed model.