Efficient Hybrid Neural Network for Automatic Modulation Recognition
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-0744-7_29
Reference18 articles.
1. Kassri, N., Ennouaary, A., Bah, S., Baghdadi, H.: A Review on SDR, Spectrum Sensing, and CR-based IoT in Cognitive Radio Networks. International Journal of Advanced Computer Science and Applications. 12, 100–121 (2021). https://doi.org/10.14569/IJACSA.2021.0120613
2. Huynh-The T, Pham QV, Nguyen TV, Nguyen TT, Ruby R, Zeng M, Kim DS (2021) Automatic Modulation Classification: A Deep Architecture Survey. IEEE Access. 9:142950–142971. https://doi.org/10.1109/ACCESS.2021.3120419
3. Kassri, N., Ennouaary, A., Bah, S.: Lightweight Hybrid Deep Learning Scheme for Automatic Modulation Classification in Cognitive Radio Networks. In: 2022 9th International Conference on Future Internet of Things and Cloud (FiCloud). pp. 113–118. IEEE (2022)
4. Zhang D, Ding W, Zhang B, Xie C, Li H, Liu C, Han J (2018) Automatic Modulation Classification Based on Deep Learning for Unmanned Aerial Vehicles. Sensors 18:924. https://doi.org/10.3390/s18030924
5. Zhu, Zhechen., Nandi, A.Kumar.: Automatic modulation classification : principles, algorithms, and applications.
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