Classification of Services through Feature Selection and Machine Learning in 5G Networks
Author:
Publisher
Allerton Press
Subject
Signal Processing,Control and Systems Engineering,Software
Link
https://link.springer.com/content/pdf/10.3103/S014641162306007X.pdf
Reference21 articles.
1. Asad, M., Qaisar, S., and Basit, A., Client-centric access device selection for heterogeneous QoS requirements in beyond 5G IoT networks, IEEE Access, 2020, vol. 8, pp. 219820–219836. https://doi.org/10.1109/access.2020.3042522
2. Shahjalal, M., Farhana, N., Roy, P., Razzaque, Md., Kaur, K., and Hassan, M., A binary gray wolf optimization algorithm for deployment of virtual network functions in 5G hybrid cloud, Comput. Commun., 2022, vol. 193, pp. 63–74. https://doi.org/10.1016/j.comcom.2022.06.041
3. Abidi, M., Alkhalefah, H., Moiduddin, K., Alazab, M., Mohammed, M., Ameen, W., and Gadekallu, T., Optimal 5G network slicing using machine learning and deep learning concepts, Comput. Standards Interfaces, 2021, vol. 76, p. 103518. https://doi.org/10.1016/j.csi.2021.103518
4. Preciado-Velasco, J.E., Gonzalez-Franco, J.D., Anias-Calderon, C.E., Nieto-Hipolito, J.I., and Rivera-Rodriguez, R., 5G/B5G service classification using supervised learning, Appl. Sci., 2021, vol. 11, no. 11, p. 4942. https://doi.org/10.3390/app11114942
5. Kafle, V., Fukushima, Yu., Martinez-Julia, P., and Miyazawa, T., Consideration on automation of 5G network slicing with machine learning, 2018 ITU Kaleidoscope: Machine Learning for a 5G Future (ITU K), Santa Fe, Argentina, 2018, IEEE, 2018, pp. 1–8. https://doi.org/10.23919/itu-wt.2018.8597639
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