Company

Huawei

15 contributions across 1 work items
15
Tdocs
1
Work items

Recent position changes

AI-synthesized from contributions · all text is paraphrased
RAN1#120 vs RAN1#119 Feb 17, 2025
NR_AIML_air
  • 9.1.2 shifted
    Huawei shifted their argument against phase information, now citing that double phase difference cannot mitigate phase errors in NLOS scenarios and that timing/power suffices. They added a new technical case against the associated ID for TRP location consistency, arguing that TRP locations change infrequently and UE-side burden from combinatorial model training would be excessive. They preserved their stance on reusing legacy IEs for LOS/NLOS indicators and added a proposal to distinguish Rel-19 timing information via timing quality indicators or a specific Rel-19 type indicator.
  • 9.1.3 shifted
    Huawei preserved its core argument against network-side associated IDs but refined its monitoring proposal by adding specific priority rules where monitoring reports take precedence over inference reports. They added new technical requirements for separating CPU counting between legacy and AI/ML CSI reporting and advocating for L1 signaling for monitoring results to address latency. The position shifted from general feasibility arguments to specific signaling layer (L1) and resource management (CPU separation) proposals.
New positions this meeting
  • 9.1.1 — Proposes expanding CSI-RS resource set sizes to 256 beams, limits associated ID to single cell, opposes Rel-17 TCI extension, defines BAI metric, and proposes discontinuous CPU occupation.
RAN1#119 vs RAN1#118bis Nov 18, 2024
NR_AIML_air
  • 9.1.1 strengthened
    Huawei shifted from a conservative approach emphasizing reuse of existing frameworks to a more aggressive stance pushing for significant enhancements and expanded capabilities.
  • 9.1.2 strengthened
    Huawei strengthened their position by expanding opposition from general complex signaling to specifically opposing tight specification of sample-based measurements and mandatory phase information.
  • 9.1.4.1 strengthened
    Huawei maintained their support for Direction A but strengthened their position by specifically favoring Option 4-1 and adding strong opposition to Direction C based on practical implementation concerns.
  • 9.1.4.2 strengthened
    Huawei expanded their opposition to cross-vendor collaboration from just Case z1 to Cases z1, z2, z3, and z5, taking a stronger stance against complexity.

Recent contributions

R1-2500089 RAN1_120 NR_AIML_air
Discussion on AI/ML for beam management
This Huawei Tdoc (R1-2500089) addresses open issues for AI/ML-based beam management in NR, presenting 40 proposals and 8 observations across data collection, inference, and performance monitoring. The document argues for expanding CSI-RS…
R1-2500090 RAN1_120 NR_AIML_air
Discussion on AI/ML for positioning accuracy enhancement
This Huawei contribution addresses open issues for AI/ML-based positioning in NR Rel-19, covering model input/output, training data collection, consistency, monitoring, and lifecycle management. The document contains 28 proposals and 11…
R1-2500091 RAN1_120 NR_AIML_air
Discussion on AI/ML for CSI prediction
This Huawei contribution analyzes the specification impacts of AI/ML-based CSI prediction in NR, presenting 4 observations and 16 proposals across training consistency, inference configuration, performance monitoring, and Life Cycle…
R1-2409395 RAN1_119 NR_AIML_air
Discussion on AI/ML for beam management
This Huawei Tdoc (R1-2409395) addresses AI/ML for NR Air Interface beam management, presenting 40 proposals and 9 observations across data collection, NW-side and UE-side models, inference, monitoring, and LCM. Key technical stances…
R1-2409396 RAN1_119 NR_AIML_air
Discussion on AI/ML for positioning accuracy enhancement
This Huawei contribution discusses AI/ML for positioning accuracy enhancement in NR, covering model input, output, training, consistency, monitoring, and lifecycle management. It contains 30 proposals and 17 observations, arguing for…
R1-2409397 RAN1_119 NR_AIML_air
Discussion on AI/ML for CSI prediction
Huawei argues against introducing network-side indications, such as associated IDs, to ensure consistency between training and inference for UE-side AI/ML models in CSI prediction, citing feasibility issues and proprietary disclosure…
R1-2409398 RAN1_119 NR_AIML_air
Discussion on AI/ML for CSI compression
This Huawei contribution discusses inter-vendor training collaboration for AI/ML-based CSI compression in NR Rel-19, focusing on Direction A (dataset/parameter exchange) and temporal domain cases. It presents 17 proposals and 11…
R1-2409399 RAN1_119 NR_AIML_air
Discussion on other aspects of the additional study for AI/ML
Huawei analyzes model identification options for two-sided AI/ML models in NR, proposing to exclude MI-Option 1 as it applies only to one-sided cases. The document details information elements for dataset and model transfers (MI-Options 2…
R1-2410326 RAN1_119 NR_AIML_air
Discussion on the LS reply to RAN2 on functionality in AI/ML
This Huawei document discusses the framework for functionality-based Life Cycle Management (LCM) for AI/ML in NR air interface, specifically addressing RAN2 questions on beam management UE-side model functionality reporting. The document…
R1-2410654 RAN1_119 NR_AIML_air
Discussion on AI/ML for CSI prediction
Huawei argues against introducing associated IDs for ensuring training/inference consistency in CSI prediction for AI/ML-enhanced NR air interface, presenting 5 observations and 2 proposals. The document demonstrates through simulation…
R1-2407653 RAN1_118bis NR_AIML_air
Discussion on AI/ML for beam management
This Huawei document presents a comprehensive analysis of AI/ML for beam management in NR, containing 38 detailed proposals and 9 observations covering data collection, inference procedures, performance monitoring, and UE capability…
R1-2407654 RAN1_118bis NR_AIML_air
Discussion on AI/ML for positioning accuracy enhancement
This Huawei document presents 25 proposals for AI/ML-based positioning accuracy enhancement in 5G NR, covering model input/output specifications, training procedures, consistency mechanisms, and lifecycle management across different…
R1-2407655 RAN1_118bis NR_AIML_air
Discussion on AI/ML for CSI prediction
Huawei argues against introducing associated IDs for ensuring consistency between training and inference in CSI prediction for AI/ML models, proposing instead UE-side performance monitoring approaches. The document contains 2 proposals and…
R1-2407656 RAN1_118bis NR_AIML_air
Discussion on AI/ML for CSI compression
This Huawei document analyzes AI/ML for CSI compression in Release 19, focusing on inter-vendor training collaboration, temporal domain extensions, and remaining specification issues from Release 18. The document contains 17 proposals and…
R1-2407657 RAN1_118bis NR_AIML_air
Discussion on other aspects of the additional study for AI/ML
This Huawei document discusses AI/ML air interface aspects focusing on model identification for two-sided models, model transfer/delivery, and UE-side training data collection. The document contains 8 proposals and 3 observations across…