Tejas Networks Limited · 9.1.3

Tejas Networks proposes using an associated ID to align training and inference conditions, arguing that variations in interference and network-side conditions like TxRU mappings degrade AI model performance. They support AI/ML model identification via Model ID in LCM mode, requiring the Network to identify models and the UE to indicate support. For data collection, they propose reusing Rel-18 Type II Doppler codebook CSI-RS configurations and separating resources for inference from those for training/monitoring. They prioritize Type 1 performance monitoring, where the UE calculates metrics and reports output only if below a Network-assigned threshold, utilizing intermediate KPIs like SGCS or NMSE.

Tejas Networks proposes using an associated ID to align training and inference conditions, mitigating performance degradation from interference variations and TxRU mapping differences. They support AI/ML model identification in LCM mode, where the Network identifies models by Model ID and the UE indicates support. For data collection, they propose reusing Rel-18 Type II Doppler codebook CSI-RS configurations and allowing the UE to report the minimum required CSI-RS instances. Regarding performance monitoring, they prioritize Type 1 (Network-configured) monitoring, proposing that the Network assigns threshold criteria and the UE reports monitoring output only when it falls below these thresholds to reduce signaling overhead. They define specific specification impacts for monitoring, including model switching, parameter updates, and fallback operations based on metrics like SGCS and NMSE.