Modulation classification method for frequency modulation signals based on the time–frequency distribution and CNN

Signal modulation classification is an important research subject in both military and civilian field. This study proposed a novel blind modulation classification method based on the time-frequency distribution and convolutional neural network (CNN). This is the first attempt to treat the time-frequency map as a picture and use an outstanding (CNN-based) algorithm in computer vision area for signal recognition. The combination offers a novel feature extraction strategy, to some extent, which also conforms to intuition. Simulation results show that the method proposed in this study is efficient and robust and enables a high degree of automation for extracting features, training weights and making decisions. Additionally, a remarkable performance emerges with small samples and repeated training, which distinguishes this method from many other classification methods.

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