RAN1 / #120 / NR_AIML_air / Verify

NEC · 9.1.2

Specification support for positioning accuracy enhancement · RAN1#120 · Source verification
the AI's delta new vs RAN1#119
NEC is a new contributor in the current meeting. They proposed deferring second-priority cases (2a/2b) to Rel-20 to focus Rel-19 efforts on first-priority cases. They added a requirement for the LMF to determine consistent sampling parameters (Nt, k) for enhanced measurements in Case 3b and supported reporting phase information from gNB to LMF. They also argued that LOS/NLOS indicators are unnecessary when model output is timing/angle information and supported using associated IDs and validity areas for consistency.
AI-synthesized from contributions · all text is paraphrased
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Contributions at RAN1#120 · 1 doc

R1-2500606 discussion not treated 3gpp.org ↗
Discussion on specification support for AIML based positioning accuracy enhancement
Position extracted by AI
NEC proposes deferring second-priority cases (2a/2b) to Rel-20 to focus Rel-19 efforts on first-priority cases (1, 3a, 3b). For model inputs, NEC requires the LMF to determine consistent sampling parameters (Nt', k) for enhanced measurements in Case 3b and supports reporting phase information from gNB to LMF. Regarding model outputs, NEC argues that LOS/NLOS indicators are unnecessary when the model output is timing/angle information, as these are derived from a 'virtual' LOS assumption. For training data, NEC proposes defining quality indicators for power information and data samples jointly, and using 'UTC time + SFN + slot index' for timestamps. To ensure consistency between training and inference, NEC supports using associated IDs and validity areas, while opposing the explicit provision of TRP location (Alternative 4) in favor of implicit methods (Alternatives 1 and 2).
Summary
NEC submits 38 proposals and 2 observations focusing on AI/ML-based positioning accuracy enhancements for NR Rel-19, prioritizing first-priority cases (Case 1, 3a, 3b) while deferring second-priority cases to Rel-20. The document addresses critical aspects of model input/output definitions, training data collection quality, timestamp synchronization, and consistency mechanisms between training and inference phases.

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

R1-2409852 discussion not treated 3gpp.org ↗
Discussion on specification support for AIML based positioning accuracy enhancement
Position extracted by AI
NEC strongly advocates FOR sample-based measurements over path-based measurements, arguing that sample-based approach provides better standardization control and avoids vendor-specific path detection algorithms. They push FOR supporting both UE-side and network-side AI/ML model deployment with comprehensive quality indicators and consistency mechanisms. NEC is AGAINST limiting phase information usage and advocates FOR flexible implementation-dependent decisions on phase information per case. They strongly support semi-supervised learning and mixed dataset approaches to improve model robustness.
Summary
This NEC contribution provides a comprehensive discussion on AI/ML for NR positioning accuracy enhancement, covering model input/output, training data collection, lifecycle management, and consistency between training and inference. The document contains 42 detailed proposals and 6 observations addressing various technical aspects of the standardization work.
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 NEC'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.