The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
CNN-Based Automatic Modulation Classification for Beyond 5G Communications
Abstract
In this letter, we propose an improved convolutional neural network (CNN)-based automatic modulation classification network (IC-AMCNet), an algorithm to classify the modulation type of a wireless signal. Since adaptive coding and modulation is widely used in wireless communication, high accuracy and short computing time of classifier is needed. Compared with the existing CNN architectures, we adjusted the number of layers and added new type of layers to comply with the estimated latency standards in beyond fifth-generation (B5G) communications. According to the simulation results, the proposed scheme significantly outperforms the previous works in terms of both classification accuracy and computing time.
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Cites
A Survey on Low Latency Towards 5G: RAN, Core Network and Caching Solutions
Parvez, I., Rahmati, A., Guvenc, I., Sarwat, A. I., & Dai, H. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2841349 · Cited by 883
Deep neural network architectures for modulation classification
Liu, X., Yang, D., & Gamal, A. E. (2017) · 2017 51st Asilomar Conference on Signals, Systems, and Computers · doi:10.1109/acssc.2017.8335483 · Cited by 367
Radio Machine Learning Dataset Generation with GNU Radio
O’Shea, T. J. (2025) · TIB Data Manager · doi:10.57702/h54kawox · Cited by 303
Terahertz-Enabled Wireless System for Beyond-5G Ultra-Fast Networks: A Brief Survey
Huq, K. M. S., Busari, S. A., Rodriguez, J., Frascolla, V., Bazzi, W., & Sicker, D. C. (2019) · IEEE Network · doi:10.1109/mnet.2019.1800430 · Cited by 217
Fast Deep Learning for Automatic Modulation ClassificationPreprint
Ramjee, S., Ju, S., Yang, D., Liu, X., Gamal, A. E., & Eldar, Y. C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.05850 · Cited by 179
Jagannath, J., Polosky, N., O'Connor, D., Theagarajan, L. N., Sheaffer, B., Foulke, S., & Varshney, P. K. (2018) · International Conference on Communications · doi:10.1109/icc.2018.8422346 · Cited by 60
Deep Hierarchical Network for Automatic Modulation Classification
Nie, J., Zhang, Y., He, Z., Chen, S., Gong, S., & Zhang, W. (2019) · IEEE Access · doi:10.1109/access.2019.2928463 · Cited by 53
Learning Action Images Using Deep Convolutional Neural Networks For 3D Action Recognition
Huynh‐The, T., Hua, C.-H., & Kim, D. (2019) · IEEE Sensors Applications Symposium (SAS) · doi:10.1109/sas.2019.8705977 · Cited by 13
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Peng, S., Sun, S., & Yao, Y.-D. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3085433 · Cited by 308
Bhardwaj, S., & Kim, D.-S. (2023) · 2023 Fourteenth International Conference on Ubiquitous and Future Networks (ICUFN) · doi:10.1109/icufn57995.2023.10199843 · Cited by 6
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Metadata
- Authors
- Hermawan, A. P., Ginanjar, R. R., Kim, D.-S., & Lee, J.-M.
- Venue
- IEEE Communications Letters
- Published
- 2020-05
- Volume
- 24
- Issue
- 5
- Pages
- 1038-1041
- ISSN
- 1089-7798 (ISSN-L)
- 1558-2558
- 2373-7891
- OpenAlex
- W3003494250
- Cited by
- 267
- References
- 8
- FWCI
- 10.51
- Type
- journal article
- Language
- en
- Open access
- closed
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