OPPO · 9.1.2

OPPO opposes supporting reporting based on phase information for Rel-19 AI-based positioning, arguing that timing and power information are sufficient and phase adds unjustified overhead. They propose using an 'associated ID' signaled from the network to ensure consistency between AI model training and inference for UE-side models (Case 1 and 2a), rather than relying solely on validity areas which fail to account for temporal network changes. OPPO argues that Rel-19 should NOT specify dedicated enhancements for performance monitoring without ground-truth labels, leaving such mechanisms to implementation. For data collection, they propose reusing legacy LPP/NRPPa signaling without specifying data formats, asserting that content is up to implementation. They further propose that the network should not specify mechanisms to deliver training data between different entities, and that UE/gNB should indicate whether results are AI-based or legacy-based in reporting.

OPPO advocates for minimal specification impact by leveraging existing protocols (LPP, NRPPa) and leaving implementation details to vendors for flexibility, while strongly opposing phase information reporting due to limited performance gains versus overhead. They push for associated ID mechanisms to ensure training/inference consistency without exposing proprietary network information, and resist specifying performance monitoring mechanisms without ground-truth labels, preferring implementation-based approaches over standardized solutions.