ZTE · 9.1.3

ZTE argues that down tilt angle and TXRU mapping should not be treated as network-side additional conditions for AI CSI prediction, citing simulation results showing that mixed datasets guarantee performance generalization with minimal SGCS impact. They propose reusing the Rel-18 MIMO CSI prediction codebook design and CQI calculation mechanism to minimize specification changes. For resource configuration, they suggest considering m=4 or m=5 slots for the separation between consecutive aperiodic CSI-RS resources to align with baseline assumptions. Regarding performance monitoring, ZTE supports Type 2 and Type 3 monitoring and proposes supporting both periodic reports after the monitoring window and event-triggered reports. Finally, they propose considering separate CSI CPU counting for AI prediction tasks from legacy CSI processing criteria to account for distinct computational loads on dedicated units.

ZTE argues that down tilt angle and TXRU mapping should not be defined as network-side additional conditions requiring an associated ID for AI CSI prediction. They present technical evidence showing that AI models generalize well across different down tilt angles, with only a 0.28% SGCS impact when using datasets from different angles. Regarding TXRU mappings, ZTE acknowledges that models trained on one configuration may suffer performance loss in another, but proposes that this can be mitigated by using a mixed dataset rather than introducing specification-level consistency constraints. Consequently, they conclude that model generalization is sufficient to guarantee performance without adding complexity to the specification for these specific parameters.