Spreadtrum · 9.1.4.1

Spreadtrum supports extending CSI compression to Spatial-Temporal-Frequency (S-T-F) domains, presenting technical evidence that S-T-F compression yields higher SGCS and UPT gains than S-F compression or Rel-16/18 codebooks. They propose using SGCS as the primary performance metric for inter-vendor training collaboration in Direction A and suggest mitigating data mismatch issues by having UEs report UE-side data to the network. They argue that Direction B suffers from unaddressable overhead concerns due to timeliness requirements for on-device inference. For CQI determination, they support Option 1b, calculating CQI based on target CSI with realistic channel measurement and potential adjustment to account for compression errors. They recommend using 3GPP statistical channel models for reference model training in Direction C to avoid field data collection complexities. Finally, they propose NW-triggered signaling for reporting historical CSI when UCI drops and support specific monitoring options based on ground-truth CSI and NW-indicated recovery CSI.

Spreadtrum strongly advocates FOR AI-based CSI Spatial-Temporal-Frequency (S-T-F) compression demonstrating it achieves over double the SGCS gain compared to spatial-frequency compression alone, and AGAINST option 5a for inter-vendor collaboration due to increased complexity. They push FOR prioritizing options that only transfer CSI generation parts to UE to protect NW proprietary information, and advocate FOR Option 1b for CQI determination with realistic channel measurement and adjustment rather than simpler approaches.