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Work item:
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Y.LRA-NS
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Subject/title:
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Lightweight real-time AI for network slicing in IMT-2020 networks and beyond
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Status:
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Under study
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Approval process:
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AAP
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Type of work item:
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Recommendation
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Version:
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New
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Equivalent number:
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-
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Timing:
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2027-07 (Medium priority)
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Liaison:
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ITU-T SG2, SG11, 3GPP SA5, ETSI NFV
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Supporting members:
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China Unicom, Purple Mountain Laboratories (PML), China Mobile, China Telecom, ZTE, BUPT
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Summary:
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With the development of IMT-2020 networks and beyond, the network demand changes more dynamically to support diverse services, which increases the complexity of network slicing and puts forward higher demands on the quality of service. Intelligent network slicing poses critical challenges for the cost of substantial increases in data and computational overhead. Optimized slice management and orchestration capabilities are required to design for green, real-time and controllable requirements.
Therefore, it is necessary to enhance network slicing mechanisms to extract small but critical datasets of IMT-2020 networks, followed by the development of lightweight AI models based on these feature datasets. Lightweight real-time AI for network slicing makes a potential solution for addressing highly efficient slicing resource utilization issues.
This Recommendation specifies the overview, requirements, framework, network function enhancements, procedures and security considerations of lightweight real-time AI for network slicing in IMT-2020 networks and beyond.
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Comment:
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-
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Reference(s):
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Historic references:
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Contact(s):
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| ITU-T A.5 justification(s): |
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First registration in the WP:
2025-07-30 13:15:12
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Last update:
2025-07-30 13:19:39
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