| Citation: | CUI Tianshu, CUI Kai, HUANG Yonghui, et al. Convolutional neural network based algorithm for automatic modulation recognition of satellite signals[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(6): 986-994. doi: 10.13700/j.bh.1001-5965.2020.0711(in Chinese)
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Automatic modulation recognition is a key technology for spatial cognitive communication system, which helps to realize adaptive signal demodulation. Although the deep neural network has the advantage of strong feature extraction, it suffers from the problems of numerous parameters and large amount of calculation, and thus is difficult to be implemented in in-orbit applications. To mitigate these problems, we propose a lightweight, high-performance convolutional neural network structure. The network first extracts the in-phase and quadrature features of the signal, then the time domain features, and finally the mean value of each channel feature for classification. The experimental results of the classification of 11 modulation methods show that when the signal-to-noise ratio is higher than 0 dB, the average recognition accuracy can reach 86.94%, which is 31.54% higher than that of traditional cumulant methods. Compared with the current deep neural network model with high recognition accuracy, the network proposed uses only less than 10% of model parameters, and increases the calculation speed by an average of 20 times on Raspberry Pi 4B.
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