Modulation recognition network of multi-scale analysis with deep threshold noise elimination
Author:
Publisher
Zhejiang University Press
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing
Link
https://link.springer.com/content/pdf/10.1631/FITEE.2200253.pdf
Reference38 articles.
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2. Chang SG, Yu B, Vetterli M, 2000. Adaptive wavelet thresholding for image denoising and compression. IEEE Trans Image Process, 9(9):1532–1546. https://doi.org/10.1109/83.862633
3. Dahap BI, Hongshu L, 2015. Advanced algorithm for automatic modulation recognition for analogue & digital signals. Proc Int Conf on Computing, Control, Networking, Electronics and Embedded Systems Engineering, p.32–36. https://doi.org/10.1109/ICCNEEE.2015.7381423
4. Doan VS, Huynh-The T, Hoang VP, et al., 2022. MoDANet: multi-task deep network for joint automatic modulation classification and direction of arrival estimation. IEEE Commun Lett, 26(2):335–339. https://doi.org/10.1109/LCOMM.2021.3132018
5. Donoho DL, 1995. De-noising by soft-thresholding. IEEE Trans Inform Theory, 41(3):613–627. https://doi.org/10.1109/18.382009
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