vivo · 9.1.2
Specification support for positioning accuracy enhancement ·
RAN1#120 · Source verification
the AI's delta
new
vs RAN1#119
vivo is a new contributor, proposing to extend agreements from 1st priority cases to 2nd priority cases, specifically supporting sample-based channel measurements for Case 2b. They added specific candidate sets for Nt ({8, 16, 24}) and k ({0, 1, 2, 3, 4, 5}) for sample-based reporting. They support using phase information (RSCP/RSCPD) to enhance positioning accuracy and model monitoring, proposing methods like PRU-assisted calibration to mitigate transceiver initial phase impacts.
AI-synthesized from contributions · all text is paraphrased
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Contributions at RAN1#120 · 1 doc
Specification support for positioning accuracy enhancement
Position extracted by AI
vivo proposes extending agreements from 1st priority cases to 2nd priority cases, specifically supporting sample-based channel measurements for Case 2b. They require reusing existing IEs for quality indicators, opposing the definition of new abstract IEs for label or channel measurement quality. vivo argues against mandating UTC time alongside SFN for Case 1, stating LMF implementation can resolve SFN wrapping ambiguity. They propose specific candidate sets for Nt' ({8, 16, 24}) and k ({0, 1, 2, 3, 4, 5}) for sample-based reporting, and oppose transmitting offset from gNB to LMF when parameters differ from recommendations. vivo supports using phase information (RSCP/RSCPD) to enhance positioning accuracy and model monitoring, proposing methods like PRU-assisted calibration or relative phase referencing to mitigate transceiver initial phase impacts. They require supporting distance ranging for Cases 2a/3a and prioritizing functionality terminology discussions to align with RAN2.
Summary
vivo presents a comprehensive contribution for AI/ML-based positioning in NR, focusing on data collection, model inference, and monitoring for Cases 1, 2a, 2b, 3a, and 3b. The document contains 36 proposals and 9 observations, advocating for the extension of agreements from 1st priority cases to 2nd priority cases, the reuse of legacy IEs for quality indicators, and specific parameter sets for sample-based channel measurements.
Prior contributions at RAN1#119 · 1 doc · Nov 18, 2024
Specification support for positioning accuracy enhancement
Position extracted by AI
vivo argues that sample-based measurements significantly outperform path-based measurements for AI/ML positioning, particularly in scenarios with limited TRPs or bandwidth, and proposes specifying sample-based reporting with defined parameters (Nt, Nt’, k). They prefer reusing existing IEs for quality indicators and assistance data to minimize specification overhead, while suggesting implicit indication via associated IDs for privacy-sensitive information. vivo supports the inclusion of phase information (RSCP/RSCPD) and distance ranging as model inputs/outputs to enhance accuracy. They emphasize the critical need for consistency between training and inference, proposing associated IDs to manage NW-side conditions like beam patterns and bandwidth alignment. Finally, they propose defining Case 1 as a new positioning method to facilitate proper assistance data handling and model monitoring procedures.
Summary
This document from vivo analyzes specification impacts for AI/ML-based positioning in NR, focusing on data collection, model inference, and consistency between training and inference. It presents simulation results demonstrating the superiority of sample-based measurements over path-based measurements and proposes specific configurations for reporting, phase information usage, and model monitoring to ensure positioning accuracy.
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 vivo's consolidated stance at RAN1#120
against their stance at RAN1#119 and classified the change as
new.
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