Automatic anomaly identification of wireless communication signal based on nonlinear analysis-Reference-Cited by-同舟云学术

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Automatic anomaly identification of wireless communication signal based on nonlinear analysis

Published:2022-12-13 Issue:1 Volume:89 Page:101-110
ISSN:0370-0046
Container-title:Proceedings of the Indian National Science Academy
language:en
Short-container-title:Proc.Indian Natl. Sci. Acad.

Author:

Mei Fengtai,Zuo Li

Funder

The Teaching Steering Committee of Vocational College Education of Ministry of Education

Publisher

Springer Science and Business Media LLC

Subject

General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology

Link

https://link.springer.com/content/pdf/10.1007/s43538-022-00142-w.pdf

Reference21 articles.

1. Baldini, G., Gentile, C., Giuliani, R., et al.: Comparison of techniques for radiometric identification based on deep convolutional neural networks. Electron. Lett. 55(2), 90–92 (2019)

2. Bhardwaj, J., Nayak, A.: Haar wavelet transform–based optimal Bayesian method for medical image fusion. Med. Biol. Eng. Comput. 58(4), 2397–2411 (2020)

3. Bohlin, E.: Foreign direct investment, information technology and economic growth dynamics in Sub-Saharan Africa. Telecommun. Policy 44(1), 101838 (2020)

4. Chen, Z., Cui, H., Xiang, J., et al.: SigNet: a novel deep learning framework for radio signal classification. IEEE Trans. Cogn. Commun. Netw. 8(2), 529–541 (2021)

5. Cheng, L., Huang, S., Xue, M., et al.: A robust localization algorithm based on NLOS identification and classification filtering for wireless sensor network. Sensors 20(22), 6634–6656 (2020)

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