Samsung · 9.1.1

Samsung proposes specific data collection contents for NW-side training, including L1-RSRPs for Set A and Set B and timestamps, conveyed via high-layer signaling. For UE-side inference, they support configurability between Alt 1 and Alt 3 for CSI-ReportConfig and introduce DL Tx IDs to ensure consistent spatial domain transmission filters between Set A and Set B. They propose introducing a Beam Accuracy Indicator (BAI) for Type 1 Option 2 performance monitoring, calculated over X CSI reports, and prefer dedicated CSI report configurations for monitoring resources. Regarding CPU handling, Samsung proposes separate CPU counting for AI/ML-based CSI reports and scaling the legacy Z timeline based on UE capability.

Samsung advocates for a flexible framework combining multiple options for UE-side model applicability (merging Options 1 and 2), supports dedicated monitoring configurations separate from inference configurations, and pushes for practical beam accuracy indicators with multiple definition alternatives. They oppose overly restrictive single-option approaches and advocate for reusing existing CSI framework mechanisms where possible while introducing necessary AI/ML-specific enhancements.

Samsung advocates for a flexible approach supporting multiple options for UE-sided model applicability reporting, favoring dedicated monitoring configurations separate from inference configurations, and supporting enhanced quantization steps for overhead reduction. They push for practical solutions that maintain consistency between training and inference while enabling both spatial and temporal beam prediction capabilities.

Samsung advocates for a comprehensive framework supporting both UE-side and NW-side AI/ML models with emphasis on: 1) Flexible applicability reporting combining multiple options rather than forcing single approach, 2) Dedicated monitoring configurations separate from inference to avoid complexity, 3) Practical reference time definitions based on actual transmission occasions rather than abstract timing, 4) Rich content options for data collection including multiple types to support different model requirements, and 5) Reuse of existing CSI framework mechanisms where possible to minimize specification impact.

Samsung, as the document moderator, advocates for: (1) merging applicability options to give RAN2 flexibility in container design while supporting both CSI-ReportConfig and inference parameter approaches, (2) comprehensive performance monitoring with beam accuracy indicators and dedicated monitoring configurations, (3) flexible reference time definition based on actual measurement occasions rather than reporting slots, (4) supporting multiple resource sets for temporal beam prediction, and (5) reusing existing CSI framework where possible while allowing necessary enhancements. Samsung pushes against overly restrictive designs that limit implementation flexibility.

Samsung advocates for practical AI/ML beam management implementations that reuse existing CSI frameworks while introducing minimal spec impact. They push FOR: reusing legacy CSI-ReportConfig structures, supporting both UE-side and NW-side models with clear associated ID mechanisms, and practical performance monitoring with beam accuracy indicators. They push AGAINST: overly complex new signaling frameworks and support maintaining compatibility with existing beam management procedures.

Samsung, as the moderator, advocates for a comprehensive AI/ML beam management framework supporting both UE-sided and NW-sided models with practical implementation considerations. They push for flexible configuration options (supporting multiple alternatives rather than premature down-selection), reuse of existing CSI frameworks where possible, and clear separation between AI/ML and legacy processing. Samsung opposes overly complex signaling mechanisms and premature standardization of implementation-specific details, favoring solutions that provide network flexibility while maintaining UE implementation freedom.

Samsung/RAN1 advocates for a flexible AI/ML functionality reporting framework that supports both CSI-ReportConfig-based inference configuration and separate inference parameter sets for applicability reporting. They push for optional network-side additional conditions and support associated ID mechanisms while maintaining that activation timing and mandatory/optional status of various parameters remain under discussion.

Samsung advocates FOR a flexible CSI framework-based approach to AI/ML functionality management with clear separation between supported, applicable, and activated functionalities. They push FOR allowing multiple CSI reports for inference based on UE capability and support associated ID usage for consistency. They are AGAINST immediate activation of CSI report configurations upon receiving Step 3, insisting on proper applicability reporting procedures first.