R1-2410424
discussion
Specification Support of AI/ML for Positioning Accuracy Enhancement
Summary
This document from Indian Institute of Technology proposes recommendations for AI/ML-based positioning accuracy enhancement in NR, comparing sample-based and path-based measurements for model training. It contains 1 proposal and 1 observation focusing on optimal measurement approaches and path requirements.
Position
IIT advocates FOR Option B (starting time based on first detectable path power) for sample-based measurements over Option A, claiming superior positioning accuracy with sample-based approaches compared to path-based measurements. They push AGAINST increasing the number of reported paths beyond 16, arguing it adds unnecessary overhead without significant measurement quality enhancement.
Key proposals
- Proposal 1 (Model input): Based on our simulations so far, we would recommend Option B for sample-based measurements. In our understanding, Option A is close to path-based measurements. The positioning accuracy that we obtained thus far are better with sample-based compared to path-based measurements
- Observation 1 (Path Based Measurements): Approximately 16 paths are sufficient to capture most of the received power