Modulation Classification Method For Frequency Modulation Signals Based On The Time-Frequency Distribution And Cnn

IET RADAR SONAR AND NAVIGATION(2018)

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Abstract
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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Key words
feature extraction, time-frequency analysis, frequency modulation, neural nets, computer vision, modulation classification method, frequency modulation signals, time-frequency distribution, signal modulation classification, blind modulation classification method, convolutional neural network, time-frequency map, CNN-based algorithm, computer vision area, signal recognition, feature extraction strategy
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