Deep learning-based satellite signal modulation identification and doppler shift impact study

With the popularization of satellite communications and the rapid development of satellite network construction, the importance of satellite communications in military security and civilian fields is increasing. In this context, automatic identification of the modulation mode of satellite signals has become a key technology, which is of great significance in the fields of spectrum monitoring and spectrum management. In this paper, a deep learning-based signal modulation recognition method is proposed for the problem of low modulation recognition accuracy of satellite signals under the influence of Doppler shift. By taking the Doppler shift as the recognition feature, the CNN-GRU neural network model is used to recognize the signal. Verified by simulation experiments under different signal-to-noise ratios, the method achieves high accuracy in recognizing signal modulation modes such as BQSK, PQSK, 32QAM and 16QAM.