Understanding the decision-making process of cnn in modulation recognition via iterative channel relevance-Reference-Cited by-同舟云学术

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Understanding the decision-making process of cnn in modulation recognition via iterative channel relevance

Published:2024-08-08 Issue:11 Volume:18 Page:8457-8468
ISSN:1863-1703
Container-title:Signal, Image and Video Processing
language:en
Short-container-title:SIViP

Author:

Chen Xin,Zhang Jiashu,Zhao Chengqiang,Cheng Lingfeng

Funder

National Natural Science Foundation of China

the 2022 Natural Science Foundation of Sichuan Province

2023 Sichuan Provincial Science and Technology Innovation Seedling Project

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11760-024-03486-6.pdf

Reference30 articles.

1. Peng, S., Sun, S., Yao, Y.-D.: A survey of modulation classification using deep learning: signal representation and data preprocessing. IEEE Trans. Neural Netw. Learn. Syst. 33(12), 7020–7038 (2021)

2. Bhatti, F.A., Khan, M.J., Selim, A., Paisana, F.: Shared spectrum monitoring using deep learning. IEEE Trans. Cognit. Commun. Netw. 7(4), 1171–1185 (2021)

3. Dobre, O.A., Abdi, A., Bar-Ness, Y., Su, W.: Survey of automatic modulation classification techniques: classical approaches and new trends. IET Commun. 1(2), 137–156 (2007)

4. Zhang, W., Feng, M., Krunz, M., Abyaneh, A.H.Y.: Signal detection and classification in shared spectrum: A deep learning approach. In: IEEE INFOCOM 2021-IEEE conference on computer communications, pp. 1–10. IEEE (2021)

5. Zhang, F., Luo, C., Xu, J., Luo, Y., Zheng, F.-C.: Deep learning based automatic modulation recognition: models, datasets, and challenges. Digit. Signal Process. 129, 103650 (2022)

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