Company
CMCC
10 contributions across 1 work items
10
Tdocs
1
Work items
Recent position changes
AI-synthesized from contributions · all text is paraphrased
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9.1.3 shiftedCMCC softened its stance on associated IDs, moving from proposing a combined solution to preferring no specification of consistency unless degradation is proven. They refined their Type 3 monitoring proposal by explicitly naming SGCS and NMSE as intermediate KPIs. They added new specifics for Type 1 monitoring, proposing two alternatives: calculation/comparison outcomes based on thresholds or recommended LCM decisions. The position shifted from exploring ID mechanisms to focusing on conditional consistency and specific monitoring outputs.
New positions this meeting
- 9.1.1 — Advocates for comprehensive AI/ML beam management support with flexible configuration options for both network-side and UE-side models, pushing for enhanced UE capabilities beyond current 64 RS limits and Top-K beam sweeping procedures.
- 9.1.2 — Advocates for sample-based measurements over path-based measurements and pushes for reusing existing legacy mechanisms where possible to minimize specification impact.
- 9.1.3 — Advocates for maximum reuse of existing AI/ML beam management mechanisms for CSI prediction and supports hybrid approaches combining associated IDs with performance monitoring, favoring intermediate KPIs over eventual KPIs.
- 9.1.4.1 — Advocates for prioritizing Direction A and B over Direction C for inter-vendor collaboration, supporting temporal domain CSI compression with demonstrated performance gains and promoting existing R16 eType-II and Rel-18 Doppler codebooks as starting points.
- 9.1.4.2 — Advocates for prioritizing MI-Option 2 and MI-Option 3 for model identification while opposing MI-Option 4 due to limited performance gains and high specification efforts. Supports studying model transfer Case z4 with standardized reference model structures.
Recent contributions
Discussion on specification support for beam management
CMCC presents 55 proposals and 4 observations regarding the specification impact of AI/ML-based beam management in NR, covering data collection, inference, and monitoring for both NW-side and UE-side models. The document addresses critical…
Discussion on specification support for positioning accuracy enhancement
CMCC discusses specification impacts for AI/ML-based positioning in NR, focusing on measurement types, data collection, and model monitoring. The document contains 20 proposals and 13 observations covering sample-based vs. path-based…
Discussion on AI/ML for CSI prediction
CMCC discusses specification impacts for AI/ML-based CSI prediction in Rel-19, focusing on training/inference consistency, performance monitoring, data collection, and inference parameters. The document presents five proposals, arguing…
Discussion on LS on applicable functionality reporting for beam management UE-sided model
CMCC analyzes the signaling procedures for applicable functionality reporting in NR AI/ML beam management, specifically addressing Steps 3-5 of the RAN2-defined lifecycle. The document presents 15 proposals and 2 observations, arguing that…
Discussion on specification support for beam management
CMCC presents 49 proposals and 2 observations regarding the specification impacts of AI/ML-based beam management for NR, covering data collection, inference, and monitoring for both NW-side and UE-side models. The document addresses…
Discussion on specification support for positioning accuracy enhancement
CMCC analyzes specification impacts for AI/ML-based positioning in NR, presenting 17 proposals and 12 observations across methodology, measurement definitions, data collection, and model monitoring. The document argues for sample-based…
Discussion on AI/ML for CSI prediction
CMCC discusses specification impacts for AI/ML-based CSI prediction in Rel-19, focusing on ensuring consistency between training and inference phases. The document presents 8 proposals covering consistency mechanisms via associated IDs and…
Discussion on AI/ML for CSI compression
CMCC presents evaluation results for AI/ML-based temporal domain CSI compression (Cases 2 and 3), demonstrating significant SGCS gains over Rel-16 and Rel-18 benchmarks. The document proposes prioritizing inter-vendor collaboration…
Discussion on other aspects of AI/ML model and data
CMCC discusses model identification options (MI-Option 2, 3, and 4) and model transfer/delivery cases for NR AI/ML, proposing to prioritize MI-Option 2 and 3 while deprioritizing MI-Option 4 due to specification effort and RAN4…
Session notes for 9.1 (AI/ML for NR Air Interface)
This is a session notes document from CMCC serving as Ad-hoc Chair for RAN1 #119 meeting on AI/ML for NR Air Interface. The document contains agreements and conclusions across 4 main areas: beam management, positioning accuracy…