Company
Spreadtrum
11 contributions across 1 work items
11
Tdocs
1
Work items
Recent position changes
AI-synthesized from contributions · all text is paraphrased
New positions this meeting
- 9.1.1 — Opposes configuring only Set B for UE inference, prefers reusing CRI/SSBRI and TCI frameworks, presents technical case against probability metrics, and requires associated ID in CSI-ReportConfig.
- 9.1.3 — Proposes using associated ID within CSI framework to ensure consistency. Prefers UE-side data collection. Supports Type 1/3 monitoring with SGCS, deprioritizing Type 2. Suggests gNB indicate association between prediction and ground-truth CSI-RS resources.
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9.1.1 shiftedSpreadtrum shifted from emphasizing UE-initiated control to favoring network-provided configurations while maintaining opposition to complex new metrics.
Recent contributions
Discussion on AIML for beam management
Spreadtrum presents 15 proposals and 6 observations regarding AI/ML for NR Beam Management, focusing on data collection, inference reporting, and performance monitoring for both UE-side and Network-side models. The document argues against…
Discussion on AIML for CSI prediction
Spreadtrum presents eight proposals for 3GPP RAN1 regarding AI/ML-based CSI prediction in NR, focusing on ensuring consistency between training and inference, defining data collection procedures, and establishing performance monitoring…
Discussion on AIML for beam management
Spreadtrum presents 15 proposals and 6 observations regarding AI/ML for NR Beam Management, focusing on UE-side and NW-side model configurations, inference reporting, and performance monitoring. The document argues for specific signaling…
Discussion on AIML for CSI prediction
Spreadtrum discusses the consistency of training and inference for UE-sided CSI prediction models, proposing the reuse of the 'associated ID' mechanism from Beam Management to ensure network-side conditions remain consistent. The document…
Discussion on AIML for CSI compression
Spreadtrum presents evaluation results for AI-based CSI Spatial-Temporal-Frequency (S-T-F) compression, demonstrating superior SGCS and UPT performance over Rel-16 and Rel-18 baselines. The document contains 8 proposals and 7 observations…
Discussion on other aspects of AI/ML model and data
Spreadtrum presents three proposals and four observations regarding AI/ML for the NR air interface in Rel-19, focusing on data collection, model transfer, and identification. The document argues that RAN1 should deprioritize certain model…
Discussion on LS on applicable functionality reporting for beam management UE-sided model
Spreadtrum provides RAN1's response to RAN2's liaison statement on applicable functionality reporting for beam management UE-sided AI/ML models, presenting 8 comprehensive proposals addressing granularity, network-side conditions,…
Discussion on AIML for beam management
Spreadtrum presents their technical positions on AI/ML for beam management in NR, covering data collection, model inference, and performance monitoring aspects. The document contains 12 proposals and 3 observations addressing both UE-side…
Discussion on AIML for CSI prediction
Spreadtrum proposes using associated IDs to ensure consistency between training and inference for CSI prediction in AI/ML-enhanced NR air interfaces. The document contains 2 main proposals focused on leveraging existing AI-BM conclusions…
Discussion on AIML for CSI compression
Spreadtrum presents evaluation results for AI-based CSI Spatial-Temporal-Frequency (S-T-F) compression showing superior performance over Rel-16 eType II codebook, and provides 5 proposals addressing inter-vendor training collaboration, CQI…
Discussion on other aspects of AI/ML model and data
Spreadtrum presents their views on AI/ML for NR air interface general aspects including data collection, model transfer/delivery, and model identification for two-sided models. The document contains 4 proposals and 4 observations…