OPPO · 9.1.3
Specification support for CSI prediction ·
RAN1#120 · Source verification
the AI's delta
new
vs RAN1#119
OPPO is a new contributor. They argue there is no consistency issue for UE-sided CSI prediction, rendering the associated ID framework unnecessary. They prioritize UE-side data collection to avoid model transfer complexity. They oppose Type 2 monitoring and propose using average NMSE for Type 3 monitoring with configurable averaging over K instances.
AI-synthesized from contributions · all text is paraphrased
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Contributions at RAN1#120 · 1 doc
On specification for AI/ML-based CSI prediction
Position extracted by AI
OPPO argues that there is no consistency issue for UE-sided CSI prediction, rendering the associated ID framework unnecessary. They propose prioritizing UE-side data collection because it avoids the complexity of model transfer and high uplink overhead associated with raw channel reporting to the network. For NW-side collection, they support reusing Rel-18 MIMO CSI frameworks and MDT-based reporting, while preferring raw channel reporting with float32 format over CSI eigenvectors. Regarding monitoring, OPPO opposes Type 2 monitoring due to overhead and proposes using average NMSE for Type 3, with configurable averaging over K monitoring instances. They also raise an FFS on whether monitoring should be UE-level or cell-level.
Summary
OPPO presents 13 proposals and 2 observations regarding AI/ML-based CSI prediction, arguing that UE-sided models face no consistency issues and prioritizing UE-side data collection over NW-side model transfer. The document proposes reusing Rel-18 CSI frameworks for data collection, specifying raw channel reporting with float32 format, and defining performance monitoring metrics based on average NMSE for Type 1 and Type 3 monitoring.
Prior contributions at RAN1#119 · 1 doc · Nov 18, 2024
On specification for AI/ML-based CSI prediction
Position extracted by AI
OPPO advocates FOR adopting the associated ID framework from BM-Case 2 to solve consistency issues in AI/ML CSI prediction, supporting flexible model-to-ID relationships where one model can work with multiple associated IDs. They push FOR further standardization work on inter-cell consistency challenges, particularly when cells have different network-side conditions from different vendors, and advocate AGAINST requiring associated IDs for network-side training scenarios.
Summary
OPPO proposes solutions for ensuring consistency between training and inference phases in AI/ML-based CSI prediction for NR air interface, addressing both intra-cell and inter-cell scenarios. The document contains 5 proposals and 3 observations focusing on associated ID frameworks and signaling mechanisms.
How this was derived
The AI extracted the "position extracted" field above directly from each Tdoc during summarization.
For the delta summary at the top, the AI compared OPPO's consolidated stance at RAN1#120
against their stance at RAN1#119 and classified the change as
new.
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