- Q. V. Pham, N. T. Nguyen, T. Huynh-The, L. B. Le, K. Lee, and W. J. Hwang, Intelligent Radio Signal Processing: A Survey, IEEE Access, 9 (2021) 83818-83850. https://doi.org/10.1109/ACCESS.2021.3087136
- Q. Zheng, X. Tian, Z. Yu, Y. Ding, A. Elhanashi, S. Saponara, and K. Kpalma, MobileRaT: A Lightweight Radio Transformer Method for Automatic Modulation Classification in Drone Communication Systems, Drones, 7 (2023) 596. https://doi.org/10.3390/drones7100596
- H. Sun, Y. Zhang, F. Wang, J. Zhang and S. Shi, SVM Aided Signal Detection in Generalized Spatial Modulation VLC System, in IEEE Access, 9 (2021) 80360-80372. https://doi.org/10.1109/ACCESS.2021.3084823
- A. Linares, B. Mejia, A. Sanchez and G. Kemper, An SVM-based Intelligible Signal Presence Detection Algorithm for FM Signals Demodulated via SDR, 2022 11th Int. Conf., Commun. Circuits Syst., (ICCCAS), Singapore, 2022, 90-95. https://doi.org/10.1109/ICCCAS55266.2022.9823981
- M. Du, P. Zhong, X. Cai, D. Bi, DNCNet: Deep Radar , Signal Denoising and Recognition, IEEE Trans. Aerosp. Electron. Syst., 58 (2022) 3549-3562. https://doi.org/10.1109/TAES.2022.3153756
- H. Yang, H. Xu, Y. Shi, Y. Zhang, and S. Zhao, A Few-Shot Automatic Modulation Classification Method Based on Temporal Singular Spectrum Graph and Meta-Learning, Appl. Sci., 13 (2023) 9858. https://doi.org/10.3390/app13179858
- X. Zhang, T. Li, P. Gong, R. Liu, and X. Zha, Modulation recognition of communication signals based on multimodal feature fusion, Sensors, 22 (2022) 6539. https://doi.org/10.3390/s22176539
- S. H. Kim, J. W. Kim, V. S. Doan, and D. S. Kim, Lightweight Deep Learning Model for Automatic Modulation Classification in Cognitive Radio Networks, IEEE Access, 8 (2020) 197532-197541. https://doi.org/10.1109/ACCESS.2020.3033989
- S. H. Kim, J. W. Kim, W. P. Nwadiugwu, and D. S. Kim, Deep Learning-Based Robust Automatic Modulation Classification for Cognitive Radio Networks, IEEE Access, 9 (2021) 92386-92393. https://doi.org/10.1109/ACCESS.2021.3091421
- T. Huynh-The, C. H. Hua, J. W. Kim, S. H. Kim, and D. S. Kim, Exploiting a Low-Cost CNN with Skip Connection for Robust Automatic Modulation Classification, 2020 IEEE Wireless Commun. Networking Conf., (WCNC), 2020, 1-6. https://doi.org/10.1109/WCNC45663.2020.9120667
- S. H. Kim, C. B. Moon, J. W. Kim, and D. S. Kim, A Hybrid Deep Learning Model for Automatic Modulation Classification, IEEE Wireless Commun. Lett., 11 (2021) 313-317. https://doi.org/10.1109/LWC.2021.3126821
- T. Huynh-The, C. H. Hua, V. S. Doan, and D. S. Kim, Accurate Modulation Classification with Reusable-Feature Convolutional Neural Network, 2021 IEEE Eighth Int. Conf. Commun. Electron., 2021, 12-17. https://doi.org/10.1109/ICCE48956.2021.9352042
- T. J. O'Shea, T. Roy, and T. C. Clancy, Over-the-Air Deep Learning Based Radio Signal Classification, IEEE J. Sel. Top. Signal Process., 12 (2018) 168-179. https://doi.org/10.1109/JSTSP.2018.2797022
- Y. Tian , D. Xu, Endong Tong, R. Sun, K. Chen and Y. Li, Toward Learning Model-Agnostic Explanations for Deep Learning-Based Signal Modulation Classifiers, IEEE Trans. Reliab., 73 (2024) 1529-1543. https://doi.org/10.1109/TR.2024.3367780
- X. He, Z. Cao, P. Ji, L. Gu, Sh. Wei and B. Fan, Eliminating the Fading Noise in Distributed Acoustic Sensing Data, IEEE Trans. Geosci. Remote Sens., 61 (2023) 5906510. https://doi.org/10.1109/TGRS.2023.3263159
- S. Dong, C. Dong, Z. Li and M. Ge, Gaussian Noise Removal Method Based on Empirical Wavelet Transform and Hypothesis Testing, 2022 3rd Int. Conf. Big Data Artif. Intell. Internet Things Eng., (ICBAIE), Xi’an, China, 2022, 24-27. https://doi.org/10.1109/ICBAIE56435.2022.9985814
- J. H. Tyler, M. M. K. Fadul, D. R. Reising and F. I. Kandah, An Analysis of Signal Energy Impacts and Threats to Deep Learning Based SEI, ICC 2022 - IEEE Int. Conf. Commun. Seoul, Korea, Republic , 2022, 2077-2083. https://doi.org/10.1109/ICC45855.2022.9838884
- N. Shajihan, Classification of stages of Diabetic Retinopathy using Deep Learning, Bournemouth University United Kingdom, 2020. https://doi.org/10.13140/RG.2.2.10503.62883
- David , Evaluation: from precision, recall and F-measure to ROC informedness, markedness and correlation, J. Mach. Learn. Technol., 2 (2011) 37-63. https://doi.org/10.48550/arXiv.2010.16061.
- V. Clerico, J. González-López, G. Agam, J. Grajal, LSTM Framework for Classification of Radar and Communications Signals, 2023 IEEE Radar Conference (RadarConf23), San Antonio, TX, USA, 2023, 1-6. https://doi.org/10.1109/RadarConf2351548.2023.10149618
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