R1-2409927
discussion
Specification support for AI/ML-based CSI prediction
From CATT
Summary
CATT's document analyzes consistency requirements between training and inference phases for AI/ML-based CSI prediction in 5G NR networks. The document presents 3 observations and 3 proposals, demonstrating through simulations that network-side conditions like antenna tilt and TXRU mapping have negligible impact, while interference distributions significantly affect performance.
Position
CATT advocates FOR relaxing consistency requirements for network-side antenna configurations (tilt angles, TXRU mapping) since they show negligible performance impact, while pushing FOR performance monitoring-based methods to handle other consistency factors like interference distributions that cannot be addressed through associated IDs. They are positioning AGAINST overly restrictive consistency requirements that would unnecessarily complicate CSI prediction implementations.
Key proposals
- Proposal 1 (Sec 1): For CSI prediction use case, conclude that there is no need to ensure consistency between training and inference regarding antenna down tilt and TXRU mapping configuration at network side
- Proposal 2 (Sec 2): For CSI prediction use case, factors other than NW-side additional conditions, e.g., various interference distributions, can impact the AI/ML-based CSI prediction performance
- Proposal 3 (Sec 3): For CSI prediction use cases, consider performance monitoring based method to ensure consistency between training and inference, further discuss NW-side performance monitoring (i.e., Type 1, 2, 3 performance monitoring) and UE-side performance monitoring