Samsung · 9.1.2

Samsung argues that full-size raw channel measurements are unsuitable for data collection due to prohibitive overhead and storage costs, proposing instead that truncated or feature-extracted measurements be used. They support explicit signaling mechanisms where UEs can notify the network of their willingness or ability to act as data providers, including notifications when specific quality conditions like SNR are not met. Samsung proposes that consistency checks between training and inference phases must occur before model selection to ensure alignment. They support the inclusion of timestamps and quality indicators alongside model outputs and define distinct monitoring metrics based on model output, input, or other measurements. Furthermore, they specify that post-monitoring behaviors, such as finetuning or fallback to legacy methods, must be clearly defined in the specification.