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Work item:
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Y.det-qos-req-ml-jrs
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Subject/title:
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QoS requirements of machine learning based joint resource scheduling to support deterministic communication services across heterogeneous networks including IMT-2020 and beyond
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Status:
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Under study [Issued from previous study period]
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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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2025-11 (High priority)
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Liaison:
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3GPP SA2, ITU-T SG12, IEEE 802.1 TSN, IETF DetNet
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Supporting members:
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University of Science and Technology Beijing, China Unicom, Korea (Republic of)
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Summary:
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The communication services are usually transmitted across heterogeneous networks with different access technologies, which makes QoS assurance for end-to-end deterministic communication services more complexed. Machine learning (ML)-based joint resource scheduling scheme could reduce the algorithm complexity due to its capability to make optimized online scheduling and resource allocation among multiple networks based on the services and networks states. Therefore, this draft new recommendation specifies the requirements, framework and operational procedures of ML-based joint resource scheduling, aiming to support deterministic communication services across heterogeneous networks including IMT-2020 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:
2023-11-15 14:46:37
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Last update:
2025-08-08 19:03:06
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