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ITU GSR 2024

ITU-T work programme

[2022-2024] : [SG13] : [Q20/13]

[Declared patent(s)]  - [Publication]

Work item: Y.3183 (ex Y.ML-IMT2020-VNS)
Subject/title: Framework for network slicing management assisted by machine learning leveraging QoE feedback from verticals
Status: Approved on 2023-01-13 [Issued from previous study period]
Approval process: AAP
Type of work item: Recommendation
Version: New
Equivalent number: -
Timing: -
Liaison: ITU-T SG9, ITU-T SG16, ETSI ENI, ETSI ZSM
Supporting members: Orange, IBM, Altice (MEO Serviços de Comunicações)
Summary: This Recommendation provides a framework for machine learning assisted network slicing management, leveraging vertical end users’ feedback on QoE, which can help achieve run-time optimisation of user perceived performance. The overall architecture, components, workflow and related APIs of this framework are specified with respect to the high-level requirements identified. A use case is provided in appendix to show an application example of this framework. Example implementations of the key APIs are also provided.
Comment: Based on the FG-ML5G Deliverables
Reference(s):
  Historic references:
Contact(s):
Qi WANG, Editor
ITU-T A.5 justification(s):
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First registration in the WP: 2020-08-04 17:28:41
Last update: 2022-11-28 15:15:55