RAN1 / #120 / NR_AIML_air / Verify

Kyocera · 9.1.1

Specification support for beam management · RAN1#120 · Source verification
the AI's delta new vs RAN1#119
Kyocera is a new contributor in this meeting. They propose distinct configuration strategies for UE-side and NW-side AI/ML models, requiring Set B to be explicitly configured for UE measurements while allowing Set A to be virtually configured. They argue against reporting full beam indices for NW-side models when M equals the resource set size, proposing instead to report only the index of the strongest beam. They require beam properties, set sizes, and resource indexing to remain consistent between training and inference phases for the same associated ID. They explicitly deprioritize Alt 4 (probability-only metrics) due to lack of ground truth validation.
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Contributions at RAN1#120 · 1 doc

R1-2500390 discussion not treated 3gpp.org ↗
Specification Support for AI/ML for Beam Management
Position extracted by AI
Kyocera proposes distinct configuration strategies for UE-side and NW-side AI/ML models, specifically requiring Set B to be explicitly configured for UE measurements while allowing Set A to be virtually configured for reference mapping. They argue against reporting full beam indices for NW-side models when M equals the resource set size, proposing instead to report only the index of the strongest beam to reduce overhead. Regarding consistency, Kyocera requires that beam properties, set sizes, and resource indexing remain consistent between training and inference phases for the same associated ID, and prioritizes defining these UE assumptions before extending associated ID applicability across cells to prevent revealing proprietary network information. For performance monitoring, they support Type 1 Option 2 metrics based on ground truth measurements (Alt 1-3) and explicitly deprioritize Alt 4 (probability-only metrics) due to the lack of ground truth validation. They further propose specific Beam Accuracy Indicator (BAI) definitions and handling mechanisms for monitoring sets that are subsets of Set A.
Summary
Kyocera submits 25 proposals and 4 observations regarding AI/ML beam management for NR Rel-19, focusing on the configuration of Sets A and B, inference reporting formats, and performance monitoring mechanisms. The document addresses consistency requirements via associated IDs, defines specific metrics for UE-side model monitoring (Type 1 Option 2), and proposes overhead reduction techniques for NW-side models.

Prior contributions at RAN1#119 · 1 doc · Nov 18, 2024

R1-2409569 discussion not treated 3gpp.org ↗
Specification Support for AI/ML for Beam Management
Position extracted by AI
Kyocera proposes that for UE-side AI/ML models, Set A be virtually configured for reference mapping while Set B is explicitly configured for measurements, requiring a new IE to associate beams with these sets. They require the introduction of an 'associated ID' within the CSI framework (nzp-CSI-RS-ResourceSet/csi-SSB-ResourceSet) to ensure consistency of network-side additional conditions, arguing that this ID should be limited to small geographical areas or single cells to avoid revealing proprietary vendor information. For inference reporting, they support 4-bit probability precision and propose studying confidence intervals defined as statistical ranges for predicted RSRP. Regarding performance monitoring, Kyocera supports Type 1 Option 2 metrics including beam prediction accuracy and RSRP differences, but deprioritizes probability-based metrics (Alt 4) due to the lack of ground truth. They propose a 'BeamCorrespondence' IE to link monitoring resources to beam indices and argue that Type 2 monitoring and NW-side monitoring should be left to vendor implementation without specification impact.
Summary
Kyocera presents a comprehensive framework for AI/ML-assisted beam management in NR Rel-19, covering configuration, inference reporting, consistency, and performance monitoring for both NW-sided and UE-sided models. The document contains 35 proposals and 4 observations, focusing on defining Set A/B configurations, introducing associated IDs for consistency, and detailing specific metrics for Type 1 performance monitoring.
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 Kyocera's consolidated stance at RAN1#120 against their stance at RAN1#119 and classified the change as new. Always verify critical claims against the original Tdocs linked above.