A deep learning based algorithm with multi-level feature extraction for automatic modulation recognition-Reference-Cited by-同舟云学术

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A deep learning based algorithm with multi-level feature extraction for automatic modulation recognition

Published:2021-08-26 Issue:7 Volume:27 Page:4665-4676
ISSN:1022-0038
Container-title:Wireless Networks
language:en
Short-container-title:Wireless Netw

Author:

Zhang Hang,Nie Ruihua,Lin Minghui,Wu Ruijuan,Xian Guo,Gong Xiaofeng,Yu Qin,Luo Ruisen

Funder

Sichuan Province Science and Technology Support Program

University-Enterprise Cooperation Projects

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems

Link

https://link.springer.com/content/pdf/10.1007/s11276-021-02758-0.pdf

Reference46 articles.

1. Ali, A., & Hamouda, W. (2014). Spectrum monitoring using energy ratio algorithm for OFDM-based cognitive radio networks. IEEE Transactions on Wireless Communications, 14(4), 2257–2268.

2. Daskalakis, S. N., Correia, R., Goussetis, G., Tentzeris, M. M., Carvalho, N. B., & Georgiadis, A. (2018). Four-PAM modulation of ambient FM backscattering for spectrally efficient low-power applications. IEEE Transactions on Microwave Theory and Techniques, 66(12), 5909–5921.

3. Li, P. (2019). Research on radar signal recognition based on automatic machine learning. Neural Computing and Applications, 32(7), 1959–1969.

4. Chen, Y., Yang, M., Long, J., Xu, D., & Blaabjerg, F. (2019). A DDS-based wait-free phase-continuous carrier frequency modulation strategy for EMI reduction in FPGA-based motor drive. IEEE Transactions on Power Electronics, 34(10), 9619–9631.

5. El-Mahdy, A. E., & Namazi, N. M. (2002). Classification of multiple M-ary frequency-shift keying signals over a Rayleigh fading channel. IEEE Transactions on Communications, 50(6), 967–974.

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