Abstract
Automatic modulation classification (AMC), whose main purpose is to recognize the received signal modulation mode under multi-signal environment and noise interference, provides the foundation for subsequent signal processing. In our paper, we propose a novel robust and real-time AMC implementation method based on deep learning (DL) and cognitive radio (CR), which can get faster recognition and higher accuracy. In this paper, it only needs 79.76us to recognize a set of data, and the accuracy is great than 90% at low signal-to-noise ratio.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China under Grant No. 61971355, 61971357, 61901390, 61771404, 61871459, in part by the Natural Science Basic Research Plan in Shaanxi Province of China under Grant 2019JM-350, and sponsored by the seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University No.CX2020164.
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Yuan, R., Xie, J. (2022). Robust and Real-Time Automatic Modulation Classification System Based on Deep Learning. In: Yan, L., Duan, H., Yu, X. (eds) Advances in Guidance, Navigation and Control . Lecture Notes in Electrical Engineering, vol 644. Springer, Singapore. https://doi.org/10.1007/978-981-15-8155-7_349
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DOI: https://doi.org/10.1007/978-981-15-8155-7_349
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