China Telecom · 9.1.1
Specification support for beam management ·
RAN1#120 · Source verification
Claude's delta
refined
vs RAN1#119
China Telecom refined their data collection proposal by adding specific payload reduction mechanisms via bitmap indications and larger quantization steps. They hardened their requirement for UE-sided models, now explicitly requiring inference reports to include Top K beams with predicted RSRP above a probability threshold. They added support for Option 3 for BM-Case 2 reference time, linking it to the latest transmission occasion of CSI-RS/SSB in Set B. Their preference for Beam prediction accuracy KPIs is preserved, with added support for configuring full Set A or using L1-RSRP differences for Top K predicted beams.
AI-synthesized from contributions · all text is paraphrased
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Contributions at RAN1#120 · 1 doc
Discussion on AI/ML for beam management
Position extracted by Claude
China Telecom supports collecting L1-RSRP, beam indices, and timestamps for NW-sided models, while proposing payload reduction via bitmap indications and larger quantization steps for differential RSRP. For UE-sided models, they oppose configuring only one resource set for Set B in BM-Case 1 and require that inference reports include Top K beams with predicted RSRP above a certain probability threshold. They support Option 3 for BM-Case 2 reference time, linking it to the latest transmission occasion of CSI-RS/SSB in Set B. For performance monitoring, they prefer Beam prediction accuracy KPIs and support configuring the full Set A for UE-assisted monitoring (Alt 1) or using L1-RSRP differences for Top K predicted beams (Alt 2). Finally, they confirm the working assumption that associated IDs ensure consistency within a cell and propose configuring associated IDs per CSI report configuration.
Summary
China Telecom submits 16 proposals addressing AI/ML beam management for NR, covering data collection, model inference, performance monitoring, and consistency mechanisms for both network-side and UE-side models. The document focuses on reducing signaling overhead for NW-sided models, defining specific reporting criteria for UE-sided inference results, and establishing metrics for performance monitoring and fallback procedures.
Prior contributions at RAN1#119 · 1 doc · Nov 18, 2024
Discussion on AI/ML for beam management
Position extracted by Claude
China Telecom advocates FOR comprehensive support of multiple data collection options for flexibility across different LCM purposes (training, monitoring, inference), beam prediction accuracy KPIs as primary performance monitoring metrics, and full Set A measurement for accurate UE-assisted performance monitoring. They push AGAINST using only single resource set configurations for Set B in UE-sided models and deprioritize link quality metrics for AI/ML model performance monitoring, arguing these don't directly reflect prediction accuracy.
Summary
China Telecom's contribution discusses AI/ML for beam management in NR, focusing on Life Cycle Management (LCM) aspects including data collection, model inference, and performance monitoring for both network-sided and UE-sided models. The document contains 16 technical proposals covering BM-Case 1 (spatial beam prediction) and BM-Case 2 (temporal beam prediction).
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 China Telecom's consolidated stance at RAN1#120
against their stance at RAN1#119 and classified the change as
refined.
Always verify critical claims against the original Tdocs linked above.