Abstract
With the development of the Internet of Things technology, the radio frequency (RF) fingerprint identification technology of wireless communication equipment has also risen, providing new ideas for network security and RF perception systems. The existing RF fingerprint identification technology is mainly based on traditional machine learning or deep learning. In the face of small sample data or data imbalance, the classification effect is not satisfactory. Therefore, in this paper, we propose the use of Few-Shot Learning (FSL) to solve the problem of radio frequency fingerprint small sample recognition. We review the current RF fingerprint identification technology and FSL methods. What’s more, we analyze some available methods from two aspects. (i) From the perspective of data, the samples of RF signal training data set can be expanded manually or by using transformation function, can also be generated by generative model; (ii) From the perspective of algorithms, prior knowledge can be used to train the new model through fine-tuning, metric, and meta-learning. Finally, we look forward to the challenges and opportunities that the RF fingerprint identification technology may face from theory and application.
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Acknowledgement
Partially Funded by Science and Technology Program of Sichuan Province (2021YFG0330), partially funded by Grant SCITLAB-0001 of Intelligent Terminal Key La-boratory of SiChuan Province, and partially Funded by Fundamental Research Funds for the Central Universities (ZYGX2019J076) ).
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Li, H., Tang, Y., Lin, D., Gao, Y., Cao, J. (2021). A Survey of Few-Shot Learning for Radio Frequency Fingerprint Identification. In: Wang, X., Wong, KK., Chen, S., Liu, M. (eds) Artificial Intelligence for Communications and Networks. AICON 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 396. Springer, Cham. https://doi.org/10.1007/978-3-030-90196-7_37
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DOI: https://doi.org/10.1007/978-3-030-90196-7_37
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