Company

Google

8 contributions across 1 work items
8
Tdocs
1
Work items

Recent position changes

AI-synthesized from contributions · all text is paraphrased
RAN1#120 vs RAN1#119 Feb 17, 2025
NR_AIML_air
  • 9.1.2 shifted
    Google shifted their focus from general flexible channel measurements to specifically proposing the extension of enhanced path-based measurement to the UE side to reduce reporting overhead. They added a new proposal to report L1-SINR alongside path-based measurements to enable network filtering. For model performance monitoring in Case 1, they refined their support for UE-side monitoring (Option A) by specifying a simple 1-bit failure indication and explicitly opposing Option B due to functional redundancy and privacy concerns. They also added support for Alternative 4, requiring explicit provision of TRP geographical coordinates from the LMF.
  • 9.1.3 shifted
    Google shifted from proposing specific associated ID management to supporting the reuse of the broader Beam Management inference and monitoring frameworks. They added a new specific metric proposal: CQI offset between predicted and ground-truth CSI. They refined their resource configuration proposal by specifying separate CSI-RS resource sets for inference and monitoring. They also added a preference for NW-triggered reports via UCI while deprioritizing event-triggered reports.

Recent contributions

R1-2500545 RAN1_120 NR_AIML_air
AI/ML based Beam Management
Google presents 26 proposals for ML-based Beam Management in NR, covering beam measurement, reporting, indication, failure recovery, and performance monitoring. The document addresses open issues for both NW-side and UE-side models,…
R1-2500546 RAN1_120 NR_AIML_air
AI/ML based Positioning
Google presents five proposals for 3GPP RAN1 regarding AI/ML-based positioning, focusing on extending enhanced path-based measurements to the UE side, reporting L1-SINR to handle measurement errors, and simplifying model monitoring. The…
R1-2500547 RAN1_120 NR_AIML_air
AI/ML based CSI Prediction
Google proposes reusing the inference configuration and performance monitoring frameworks established for AI/ML-based Beam Management (BM) to ensure consistency for AI/ML-based CSI prediction. The document outlines six specific proposals…
R1-2410149 RAN1_119 NR_AIML_air
AI/ML based Beam Management
This 3GPP RAN1 technical document from Google presents 27 proposals for AI/ML-based beam management enhancements in 5G NR, covering beam measurement, reporting, indication, failure recovery, and performance monitoring across both…
R1-2410150 RAN1_119 NR_AIML_air
AI/ML based Positioning
Google's contribution discusses ML-based positioning for NR, presenting 4 key proposals covering training data collection, model monitoring, and measurement configurations. The document builds on previous RAN1 agreements and focuses on…
R1-2410151 RAN1_119 NR_AIML_air
AI/ML based CSI Prediction
Google proposes enhancements for AI/ML based CSI prediction in NR air interface, focusing on UE-side model consistency between training and inference. The document contains 2 main proposals addressing CSI-RS configuration reporting and…
R1-2410152 RAN1_119 NR_AIML_air
AI/ML based CSI Compression
Google's technical document on ML-based CSI compression for 5G NR presents 15 proposals covering CSI report content, processing units, model monitoring, data collection, and inter-vendor collaboration. The document addresses key aspects of…
R1-2410153 RAN1_119 NR_AIML_air
AI/ML Model and Data
Google presents a technical document on AI/ML model identification and data collection for NR air interface, proposing 8 specific proposals covering model identification types, UE data collection procedures, and ID configuration schemes.…