An efficient automatic modulation recognition using time–frequency information based on hybrid deep learning and bagging approach
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Hardware and Architecture,Human-Computer Interaction,Information Systems,Software
Link
https://link.springer.com/content/pdf/10.1007/s10115-023-02041-y.pdf
Reference49 articles.
1. Wei S, Sun Z, Wang Z et al (2023) An efficient data augmentation method for automatic modulation recognition from low-data imbalanced-class regime. Appl Sci 13:3177. https://doi.org/10.3390/app13053177
2. Zhang D, Lu Y, Li Y et al (2023) Frequency learning attention networks based on deep learning for automatic modulation classification in wireless communication. Pattern Recognit 137:109345
3. Jdid B, Hassan K, Dayoub I et al (2021) Machine learning based automatic modulation recognition for wireless communications: a comprehensive survey. IEEE Access 9:57851–57873. https://doi.org/10.1109/ACCESS.2021.3071801
4. Chen W, Xie Z, Ma L et al (2019) A faster maximum-likelihood modulation classification in flat fading non-Gaussian channels. IEEE Commun Lett 23:454–457
5. Krayani A, Alam AS, Calipari M et al (2021) Automatic modulation classification in cognitive-IoT radios using generalized dynamic Bayesian networks. In: 7th IEEE world forum internet things WF-IoT 2021 pp 235–240https://doi.org/10.1109/WF-IoT51360.2021.9594936
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