Company
CATT
9 contributions across 1 work items
9
Tdocs
1
Work items
Recent position changes
AI-synthesized from contributions · all text is paraphrased
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9.1.1 strengthenedCATT refined their stance on associated ID configuration, specifying it should be at the CSI-Report level rather than requiring similar DL Tx beam properties. They hardened their opposition to extending Rel-17 TCI state activation for multiple future time instances in BM-Case 2, citing complexity. New technical arguments were added regarding the benefit of aligning Rx beam information for NW-sided models and the introduction of an enhanced CPU pool distinct from legacy CPUs. Their support for event-based performance monitoring linked to inference reports is a new addition.
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9.1.3 shiftedCATT preserved its stance on negligible impact of tilt/TXRU but refined its position by adding specific proposals for a new processing unit type or enhanced CPU separate from the legacy pool. They added a requirement to distinguish AI/ML CSI reports via a new report quantity or identifier. Their monitoring preference was consolidated to explicitly prefer SGCS for Type 1 and Type 3 while deprioritizing Type 2, moving from general monitoring advocacy to specific metric and resource definitions.
Recent contributions
Discussion on AI/ML-based beam management
This document from CATT presents 49 proposals regarding AI/ML-based beam management for NR, covering consistency issues, UE-sided and NW-sided model inference, performance monitoring, and CSI processing enhancements. It addresses specific…
Discussion on AI/ML-based positioning
This document from CATT discusses specification impacts for AI/ML-based positioning in Rel-19, covering data collection, model inference, performance monitoring, and consistency issues across five positioning cases. It contains 37…
Discussion on AI/ML-based CSI prediction
CATT presents simulation results demonstrating that antenna tilt angles and TXRU mappings have negligible impact on UE-sided CSI prediction performance, concluding that strict consistency between training and inference for these parameters…
Discussion on reply LS on applicable functionality reporting for beam management
CATT provides a comprehensive response to RAN2's liaison statement on AI/ML beam management for UE-side models, presenting 12 detailed proposals covering associated ID configuration, inference parameter handling, and CSI reporting…
Specification support for AI/ML-based beam management
CATT presents a comprehensive technical contribution on AI/ML-based beam management for 5G NR Rel-19, covering both UE-sided and network-sided models with 39 detailed proposals addressing configuration, inference, reporting, and…
Specification support for AI/ML-based positioning
CATT's comprehensive technical document presents 35 proposals and 7 observations for AI/ML-based positioning across the NR air interface, covering data collection, model inference, performance monitoring, and consistency issues for all…
Specification support for AI/ML-based CSI prediction
CATT's document analyzes consistency requirements between training and inference phases for AI/ML-based CSI prediction in 5G NR networks. The document presents 3 observations and 3 proposals, demonstrating through simulations that…
Additional study on AI/ML-based CSI compression
This CATT document provides extensive analysis and evaluation results for AI/ML-based CSI compression inter-vendor training collaboration, containing 27 proposals and 25 observations covering various approaches including Direction A…
Additional study on AI/ML for other aspects
This 3GPP RAN1 technical document from CATT addresses various aspects of AI/ML for NR air interface, focusing on model identification, model transfer/delivery, and data collection issues. The document contains 15 proposals and 3…