Lenovo · 9.1.2

Lenovo proposes extending sample-based measurement definitions to UE-based Cases 1 and 2b, requiring the definition of reference time and parameters {Nt, Nt’, k}. They support the inclusion of DL and UL Channel Impulse Response (CIR) measurements, as well as legacy measurements like RSTD and RSRP, as model inputs for fingerprinting. Lenovo requires the reuse of legacy LOS/NLOS indicator frameworks for AI/ML model outputs and supports Multi-RTT timing measurements for LMF-side models. They propose introducing a new UE-based positioning method for Direct AI/ML Case 1 and support Alternative 2 for signaling TRP geographical coordinates (Info #7). For training data, Lenovo supports indications for labelled versus unlabelled data samples and confirms working assumptions that labels can be generated by PRUs, Non-PRU UEs, or the LMF. Finally, they propose reporting model monitoring outcomes based on positioning accuracy and introducing mechanisms for UEs to retrieve ground truth labels from the LMF.

Lenovo advocates FOR a hybrid measurement approach supporting both sample-based and path-based timing representations depending on the use case, with strong emphasis on reusing legacy frameworks where possible (LOS/NLOS indicators, assistance data signaling). They push FOR comprehensive training data collection frameworks with flexible entity assignments and AGAINST mixed measurement approaches within single use cases (e.g., using sample-based in training but path-based in inference).