Ericsson · 9.1.1

Ericsson proposes configuring the associated ID at the CSI-ResourceSet level rather than the CSI-ReportConfig level to preserve fundamental CSI framework assumptions and enable predictions beyond current resourceSet size limits. They require support for separate CSI-ResourceConfigIds for Set A and Set B, and propose extending aperiodic resource sets to 64 NZP CSI-RS resources. For inference reporting, Ericsson supports adaptive Top-K values and the inclusion of probability and RSRP confidence intervals to handle model uncertainty and data drift. They argue for joint activation of monitoring and inference configurations to minimize signaling overhead and propose specific performance metrics based on Top-1/Top-K alignment. For NW-sided models, they propose mechanisms to reduce reporting overhead, including pre-processing Set B beams and omitting duplicated or unstable training data samples.

Ericsson advocates for a comprehensive AI/ML framework that leverages existing CSI mechanisms with minimal specification impact while maximizing functionality. They strongly push FOR: (1) expanding aperiodic CSI-RS resources from 16 to 64 beams to enable practical AI/ML deployment, (2) uncertainty quantification in UE predictions to enable trustworthy AI/ML, (3) adaptive Top-K beam selection based on model confidence, and (4) extensive overhead reduction mechanisms for NW-sided models. They push AGAINST complex new signaling frameworks, favoring reuse of existing CSI infrastructure with targeted enhancements.