ZTE · 9.1.3

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.

ZTE advocates FOR a systematic approach that prioritizes identifying additional conditions before rushing into detailed consistency solutions, and FOR reusing proven AI beam prediction mechanisms. They are AGAINST introducing new associated IDs for scenarios/carrier frequency, arguing these have limited performance impact (less than 2% degradation) and can be handled through existing configurations or UE implementation.