Deep-learning-based digital signal modulation mode identification

Kai He; Lintao Yu; Quan Liu

doi:10.1117/12.2668026

20 April 2023 Deep-learning-based digital signal modulation mode identification

Kai He, Lintao Yu, Quan Liu

Proceedings Volume 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022); 126022W (2023) https://doi.org/10.1117/12.2668026
Event: International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 2022, Changchun, China

Abstract

In modern electronic warfare, in order to correctly identify the intercepted enemy signals, it is first necessary to identify the modulation system used by the enemy signals and correctly demodulate them in order to obtain the information transmitted by the enemy and seize the opportunity in informationized warfare. Aiming at the low recognition rate of digital signal modulation recognition under low signal-to-noise ratio, the recognition model suitable for signal constellation diagram is improved on the basis of the classical convolutional neural network GoogLeNet network, and a recognition model suitable for signal constellation diagram is constructed. Experimental results show that when the signal-to-noise ratio SNR=5dB, the average classification accuracy of the 12 hybrid modulation types can reach 95.28%, and the classification accuracy of the improved GoogLeNet is increased by 1.1%.

Citation Download Citation

Kai He, Lintao Yu, and Quan Liu "Deep-learning-based digital signal modulation mode identification", Proc. SPIE 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 126022W (20 April 2023); https://doi.org/10.1117/12.2668026

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