Abstract
Automatic modulation recognition (AMR) of communication signals is an important research topic in the processing of intercepted signals. In this paper, aiming for the automatic recognition of modulated signals, we propose a feature learning and classification method based on the high-order cumulants, which effectively suppresses Gaussian white noise. Six digital modulation schemes including BPSK, QPSK, 8PSK, 8QAM, 16QAM, and 64QAM can be recognized by comparing the feature with the threshold. During the experiments, we plot the confusion matrix under different conditions. Moreover, it is derived and verified with simulation experiments and actual data acquisition.
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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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Li, H., Wu, H., Zhen, Q., Liu, Y., Elhanash, A., Saponara, S. (2023). Digital Modulation Recognition Method Based on High-Order Cumulant Feature Learning. 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_38
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DOI: https://doi.org/10.1007/978-3-031-30333-3_38
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