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AI-Based Autonomous Control, Management, and Orchestration in 5G: From Standards to Algorithms

descriptionPublicationkeyboard_double_arrow_right Article 01 Nov 2020 Spain Institute of Electrical and Electronics Engineers (IEEE)IEEE Network, volume 34, pages 14-20 (issn: 0890-8044, eissn: 1558-156X,

Authors: Dario Bega; Marco Gramaglia; Ramon Perez; Marco Fiore; Albert Banchs; Xavier Costa-Perez;

doi: 10.1109/mnet.001.2000047 , 10.5281/zenodo.6548574 , 10.5281/zenodo.6548575

handle: 10016/31770

AI-Based Autonomous Control, Management, and Orchestration in 5G: From Standards to Algorithms

- Summary
- Subjects
- References
  (20)
- Related research
  (5)
- Metrics

Abstract

While the application of Artificial Intelligence (AI) to 5G networks has raised a strong interest, standard solutions to bring AI into 5G systems are still in their infancy and have a long way to go before they can be used to build an operational system. In this paper, we contribute to bridging the gap between standards and a working solution, by defining a framework that brings together the relevant standard specifications and complements them with additional building blocks. We populate this framework with concrete AI-based algorithms that serve different purposes towards developing a fully operational system. We evaluate the performance resulting from applying our framework to control, management and orchestration functions, showing the benefits that AI can bring to 5G systems. This work was supported by the H2020 5G-TOURS European project (Grant Agreement No. 856950).

Country

Spain

Related Organizations

Carlos III University of Madrid
Spain
Madrid Institute for Advanced Studies
Spain
Instituto de Salud Carlos III
Spain

Subjects by Vocabulary

Microsoft Academic Graph classification: Computer science Control (management) Bridging (programming) Operational system Autonomous control Orchestration (computing) Algorithm 5G

ACM Computing Classification System: GeneralLiterature_MISCELLANEOUS

Keywords

Artificial intelligence, History, Telecomunicaciones, Computer Networks and Communications, Data analysis, Hardware and Architecture, 3GPP, Engines, Prediction algorithms, Software, Forecasting, Information Systems

20 references, page 1 of 2

[1] 3GPP TS 23.288 v16.1.0, “Architecture Enhancements for 5G System (5GS) to Support Network Data Analytics Services (Release 16),” Jun. 2019.

[2] 3GPP TS 28.533 v16.0.0, “Management and Orchestration of Networks and Network Slicing; Management and Orchestration Architecture (Release 16),” Jun. 2019.

[3] O-RAN Alliance White Paper, “O-RAN: Towards an Open and Smart RAN,” Oct. 2018.

[4] ETSI White Paper No. 32, “Network Transformation; (Orchestration, Network and Service Management Framework),” Oct. 2019.

[5] 3GPP TR 28.890 v16.0.0, “Study on integration of Open Network Automation Platform (ONAP) and 3GPP management for 5G networks (Release 16),” Mar. 2019.

[6] J. Wang, J. Tang, Z. Xu, Y. Wang, G. Xue, X. Zhang, and D. Yang, “Spatiotemporal modeling and prediction in cellular networks: A big data enabled deep learning approach,” in Proc. of IEEE INFOCOM, Atlanta, GA, May 2017.

[7] C. Zhang and P. Patras, “Long-term mobile traffic forecasting using deep spatio-temporal neural networks,” in Proc. of ACM Mobihoc, Los Angeles, CA, Jun. 2018.

[8] C. Marquez, M. Gramaglia, M. Fiore, A. Banchs, C. Ziemlicki, and Z. Smoreda, “Not All Apps Are Created Equal: Analysis of Spatiotemporal Heterogeneity in Nationwide Mobile Service Usage,” in Proc. of ACM CoNEXT, Seoul/Incheon, South Korea, Nov. 2017. [OpenAIRE]

[9] D. Bega, M. Gramaglia, M. Fiore, A. Banchs, and X. Costa-Pe´rez, “Deepcog: Optimizing resource provisioning in network slicing with ai-based capacity forecasting,” IEEE Journal on Selected Areas in Communications, vol. 38, no. 2, pp. 361-376, Feb. 2020.

[10] K. Hara, H. Kataoka, and Y. Satoh, “Can spatiotemporal 3D CNNs retrace the history of 2D CNNs and ImageNet?” in Proc. of the IEEE conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, Nov. 2018.

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Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	7
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%

Usage byUsageCounts

visibility	views	126
download	downloads	295

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	7
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%