Samsung · 9.1.4.2
Other aspects of AI/ML model and data ·
RAN1#119 · Source verification
Claude's delta
new
vs RAN1#118bis
Samsung joined the discussions as a new participant, aligning with the general trend toward simplified approaches while specifically supporting standardized reference models.
AI-synthesized from contributions · all text is paraphrased
Every position summary on this site is generated by an AI from the actual Tdoc contributions. This page shows you the exact source documents Claude read to produce the summary above, so you can verify it yourself. Click any Tdoc ID to view its detail page, or click "3gpp.org ↗" to read the original on the official 3GPP server.
Contributions at RAN1#119 · 1 doc
Views on additional study for other aspects of AI/ML model and data
Position extracted by Claude
Samsung argues that explicit model identification is not required to ensure consistency between model training and inference, proposing instead that the indication of associated IDs for network-side additional conditions is sufficient. They propose studying MI-Option1 for model-level LCM management, including timeline control and processing unit occupancy awareness, while supporting Type B1 identification procedures where the network indicates NW-side additional conditions and the UE identifies compatible models. For functionality-based LCM, Samsung proposes using boundary conditions in UE capability reports to limit complexity without exposing proprietary model details. Regarding data and model transfer, Samsung deprioritizes data delivery to external OTT servers or non-gNB/LMF entities due to privacy and proprietary implementation risks, and deprioritizes non-transparent model transfer Case z1 due to cross-vendor collaboration burdens. Finally, they propose studying Case z4 (parameter transfer for known model structures) and starting model identification studies from MI-Option4 for standardized reference models.
Summary
Samsung analyzes model identification and data handling for AI/ML in NR, presenting 13 proposals and 5 observations across model-level management, two-sided model consistency, and data privacy. The document argues that explicit model identification is unnecessary for ensuring training-inference consistency, proposing instead the use of associated IDs for network-side additional conditions, while deprioritizing external data delivery and non-transparent model transfers due to proprietary and cross-vendor collaboration complexities.
Prior contributions
Samsung has no prior contributions to 9.1.4.2 in the meetings currently tracked. This is either a new contributor to this sub-topic or the earliest meeting in our history.
How this was derived
Claude extracted the "position extracted" field above directly from each Tdoc during summarization.
For the delta summary at the top, Claude compared Samsung's consolidated stance at RAN1#119
against their stance at RAN1#118bis and classified the change as
new.
Always verify critical claims against the original Tdocs linked above.