Deep-Learning Based Mobile-Traffic Forecasting for Resource Utilization in 5G Network Slicing-Reference-Cited by-同舟云学术

Deep-Learning Based Mobile-Traffic Forecasting for Resource Utilization in 5G Network Slicing

Published:2021 Issue: Volume: Page:410-424
ISSN:2194-5357
Container-title:Advances in Intelligent Systems and Computing
language:
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Author:

Gupta Rohit Kumar,Ranjan Amit,Moid Md Ashraf,Misra Rajiv

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-76736-5_38

Reference20 articles.

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2. Association, G.S., et al.: 5g network slicing for cross industry digitization. 5G Slicing Association, Tech. Rep (2018)

3. Bega, D., Gramaglia, M., Fiore, M., Banchs, A., Costa-Perez, X.: Deepcog: Cognitive network management in sliced 5g networks with deep learning. In: IEEE INFOCOM 2019-IEEE Conference on Computer Communications. pp. 280–288. IEEE (2019)

4. Choyi, V.K., Abdel-Hamid, A., Shah, Y., Ferdi, S., Brusilovsky, A.: Network slice selection, assignment and routing within 5g networks. In: 2016 IEEE Conference on Standards for Communications and Networking (CSCN). pp. 1–7. IEEE (2016)

5. Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)

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