TCL · 9.1.2

TCL prefers Option A-2 for label-based model performance monitoring in Case 1 to minimize overhead, arguing that the target UE can autonomously derive ground truth labels from position calculation assistance data. They propose that monitoring outcomes be signaled only upon performance deterioration and recommend that label-free monitoring metrics be calculated at the model inference entity. For training data collection in Case 3b, TCL proposes down-selecting between UE-side validation via implementation or LMF-side validation using immobility duration information. To ensure consistency between training and inference, TCL proposes introducing AI-specific reference signal configurations for PRS and SRS, allowing the UE to distinguish between AI-based and non-AI-based measurements. Regarding sensitive location data (info #7), TCL prefers Alternative 1, where geographical coordinates are provided implicitly via an associated ID, and proposes down-selecting whether this ID maps to a set of TRP coordinates or a single TRP coordinate.

TCL advocates FOR Option A-2 for label-based model monitoring to reduce data transfer overhead, supports AI-specific reference signal configurations with hierarchical resource type structures, and promotes explicit indication of critical assistance data IEs for consistency. They push FOR UE-side validation in training data association and configurable offset approaches for sample timing determination, while arguing AGAINST high-overhead options that require multiple measurement data transfers.