Toward a Layer-2 Trigger for AI/ML Lifecycle Management in 6G

Abstract:3GPP has progressively expanded AI/ML lifecycle management in the radio access network, from one-sided model control to Release 20 support for two-sided CSI-feedback model pairing. Yet a basic control question remains: when monitoring detects degradation, how quickly must a corrective action take effect? To expose this dependency, we stress-test three activation and rollback strategies in a surrogate regime-shift environment using 150 independently trained PPO candidate policies, each evaluated over 20 matched noise realizations. We add 0-40 control-step delay only to corrective lifecycle commands. With no added delay, stability-gated blending reduces mean post-shift cumulative SLA deficit from 47.02 to 7.57 violation-steps relative to hard cutover; at 40 steps, the deficit rises to 44.38, only 5.6% below the hard-cutover baseline. KPI-threshold rollback loses its advantage within only a few control intervals, while blending degrades more gradually. These results do not set a physical 6G latency bound. They show why timing requirements matter for corrective actions: lifecycle performance depends on when the command takes effect. Motivated by Layer-1/Layer-2 Triggered Mobility, we examine a standards split in which Layer 3 retains lifecycle configuration while a compact Layer 2 trigger is considered only for the latency-critical subset, together with pair-consistency, local-fallback, and security/freshness requirements.

6 pages, 1 figure, 1 table. Submitted to IEEE Communications Standards Magazine

Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG); Signal Processing (eess.SP)

arXiv:2609.14517 [cs.NI]

(or arXiv:2609.14517v1 [cs.NI] for this version)

https://doi.org/10.48550/arXiv.2609.14517 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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