Abstract
In the real wireless communication applications, there are always existing multiple signal modulation schemes. To complete the demodulation of modulated signals, it is necessary to understand the signal modulation method, so it is important for the signal receiver to have the ability to automatically identify the signal modulation schemes. In this paper, for six common digital modulation schemes, each modulation scheme modulates one hundred signals respectively, then extracts five feature parameters from each signal, and finally classifies them using back propagation (BP) neural network. The experimental results illustrate that the recognition accuracy can reach more than 90% when SNR > 10 dB. The model structure is simple and practical, and it can meet the requirements of automatic recognition of modulation schemes.
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Acknowledgements
This research was supported by Shandong Provincial Natural Science Foundation (Grant ZR2020MF151), CAAC Key Laboratory of General Aviation Operation (Civil Aviation Management Institute of China) (Grant CAMICKFJJ-2020-2), National Natural Science Foundation of China (Grant U1933130 and 71731001), and Research and Demonstration of Key Technologies for the Air-Ground Collaborative and Smart Operation of General Aviation (Grant 2022C01055).
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Sun, Z., Wu, H., Zheng, Q., Liu, Y., Elhanashi, A., Saponara, S. (2023). Modulation Recognition Based on BP Neural Network. In: Berta, R., De Gloria, A. (eds) Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2022. Lecture Notes in Electrical Engineering, vol 1036. Springer, Cham. https://doi.org/10.1007/978-3-031-30333-3_46
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DOI: https://doi.org/10.1007/978-3-031-30333-3_46
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