RAN1 / #119 / NR_AIML_air / Verify

ZTE · 9.1.4.2

Other aspects of AI/ML model and data · RAN1#119 · Source verification
Claude's delta strengthened vs RAN1#118bis
ZTE provided more specific technical justification for opposing MI-Option 2 with concrete resource overhead figures (1-10GB transfers) and added explicit support for Case z4.
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

R1-2409483 discussion not treated 3gpp.org ↗
Discussion on other aspects of AI/ML model and data
Position extracted by Claude
ZTE presents a technical case against MI-Option 2 (dataset transfer), citing huge resource overhead, large latency, and potential performance degradation due to backbone misalignment. They prefer MI-Option 4 (standardization of reference UE-part model) and MI-Option 3 (model transfer), specifically prioritizing Type B model identification and model transfer Case z4 (network-trained parameters for known structure) for two-sided models. ZTE proposes that the dataset ID serves as the model ID in MI-Option 2 and that dataset transfer mechanisms be handled by higher layer signaling under RAN2 scope. They argue that specification impacts for reference model structures should be studied within the CSI compression agenda item to avoid duplication. Finally, they propose studying the timeline for model readiness and the feasibility of partial parameter transfer for Case z4.
Summary
ZTE analyzes model identification options for two-sided AI/ML models in NR, arguing against dataset transfer (MI-Option 2) due to high overhead and latency, while favoring model parameter transfer (MI-Option 3) and standardization of reference models (MI-Option 4). The document contains 14 proposals and 8 observations, prioritizing Type B model identification and specific model transfer cases (z4) for Rel-19 studies.

Prior contributions at RAN1#118bis · 1 doc · Oct 14, 2024

R1-2407800 discussion not treated 3gpp.org ↗
Discussion on other aspects of AI/ML model and data
Position extracted by Claude
ZTE strongly advocates AGAINST MI-Option 2 (dataset transfer) due to feasibility concerns including huge resource overhead (1-10GB datasets vs 7-100MB models), large latency, and high UE power consumption. They strongly support MI-Option 3 (model transfer) and MI-Option 4 (standardized reference models) as more practical alternatives. ZTE pushes FOR prioritizing model transfer case z4 with known model structures and Type B model identification over Type A, while advocating for two-sided models over UE-side models for Rel-19 normative work.
Summary
ZTE's contribution analyzes AI/ML model identification and transfer options for NR air interface, comparing dataset transfer (MI-Option 2), model transfer (MI-Option 3), and standardized reference models (MI-Option 4). The document contains 12 proposals and 5 observations across model identification, model transfer/delivery, and data collection aspects.
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 ZTE's consolidated stance at RAN1#119 against their stance at RAN1#118bis and classified the change as strengthened. Always verify critical claims against the original Tdocs linked above.