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.