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Discussion on specification support for AI/ML-based beam management
Futurewei presents 8 proposals for Rel-19 AI/ML-based beam management, focusing on specification support for performance monitoring, model inference, data collection, and RRC parameter configuration. The document argues for reusing the existing CSI framework to minimize…
Futurewei proposes reusing the existing CSI framework extensively to reduce specification effort for Rel-19 AI/ML-based beam management. They oppose supporting Opt 3 and Opt 4 for UE-sided model inference reports, arguing that defining…
AI/ML for CSI prediction
Ericsson presents proposals for the normative phase of UE-sided CSI prediction in Rel-19, focusing on functionality-based LCM, data collection, and performance monitoring. The document contains 14 proposals and 18 observations, arguing that no specification enhancements are…
Ericsson proposes reusing the functionality-based LCM framework from UE-sided beam management use cases for CSI prediction, specifically leveraging legacy CSI-RS configurations with enhancements for training data association. They require…
AI/ML for Positioning Accuracy Enhancement
Ericsson presents a comprehensive contribution on AI/ML for NR positioning accuracy enhancement, focusing on integrating AI/ML methods with existing protocols, defining model inputs/outputs, and establishing training data collection and monitoring frameworks. The document…
Ericsson proposes integrating AI/ML positioning into existing DL-TDOA and UL-TDOA/multi-RTT frameworks rather than defining new procedures, assigning procedural decisions to RAN2 and RAN3. They require the use of total-power PDP inputs…
Discussion on AI/ML-based beam management
ZTE proposes functionality-based LCM without model ID signaling for AI/ML beam management, emphasizing overhead reduction through bitmap reporting and threshold-based beam selection. The document outlines specific mechanisms for NW-side data collection, UE-side inference…
ZTE proposes utilizing functionality-based LCM without model ID based signaling for AI/ML beam management, arguing that model transfer challenges diminish the need for model-ID-based approaches. They support bitmap-based beam information…
Discussion on AI/ML-based positioning enhancement
ZTE presents 32 proposals and 3 observations regarding AI/ML-based positioning enhancements for NR Rel-19, focusing on model input definitions, phase information utility, and monitoring procedures. The document argues for reusing existing legacy signaling structures (such as…
ZTE proposes reusing existing legacy signaling structures, such as 'measurementReferenceTime' and 'NR-TimeStamp', to minimize specification impact for AI/ML positioning timestamps and quality indicators. They argue against introducing a…
Discussion on specification support for AI CSI prediction
This document from ZTE analyzes the specification support for AI-based CSI prediction in Rel-19, concluding that down tilt angle and TXRU mapping do not require additional network-side conditions due to model generalization capabilities. It presents seven proposals covering the…
ZTE argues that down tilt angle and TXRU mapping should not be treated as network-side additional conditions for AI CSI prediction, citing simulation results showing that mixed datasets guarantee performance generalization with minimal…
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 resource set sizes beyond the legacy…
Huawei proposes expanding CSI-RS resource set sizes to up to 256 beams to support AI/ML training and inference, rejecting legacy limits of 64. They require the associated ID for UE-side models to be strictly limited within a single cell to…
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 observations, primarily arguing for the…
Huawei argues against the necessity of phase information for AI/ML model input, citing that double phase difference cannot mitigate phase errors in NLOS scenarios and that timing/power suffices for performance. They oppose the introduction…
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 Management (LCM). The document argues that…
Huawei argues that generalized performance in CSI prediction can be achieved with mixed datasets (Generalization Case 3), thereby proposing that no explicit NW indications or associated IDs are needed for training/inference consistency.…
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 configuring only Set B for UE-side…
Spreadtrum opposes configuring only Set B for UE-side inference (Proposal 1) to prevent ambiguity in input-output correspondence between training and inference phases. They prefer reusing existing standard mechanisms, such as CRI/SSBRI for…
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 mechanisms. The document argues for reusing…
Spreadtrum proposes using an associated ID, configured within the CSI framework, to ensure consistency between training and inference for UE-sided CSI prediction models, arguing that performance monitoring-based approaches cause…
Discussion on AI/ML-based beam management
This document from CATT presents 49 proposals regarding AI/ML-based beam management for NR, covering consistency issues, UE-sided and NW-sided model inference, performance monitoring, and CSI processing enhancements. It addresses specific configuration mechanisms for Set A and…
CATT concludes that defining similar properties for DL Tx beams associated with an ID is unnecessary for UE-sided models, proposing instead that the associated ID be configured at the CSI-Report level. They argue that aligning Rx beam…
Discussion on AI/ML-based positioning
This document from CATT discusses specification impacts for AI/ML-based positioning in Rel-19, covering data collection, model inference, performance monitoring, and consistency issues across five positioning cases. It contains 37 proposals and 1 observation aimed at defining…
CATT proposes that timing information for a channel measurement be associated with only one quality indicator to reduce overhead, and that LMF provide quality thresholds to filter low-quality training samples. They support sample-based…
Discussion on AI/ML-based CSI prediction
CATT presents simulation results demonstrating that antenna tilt angles and TXRU mappings have negligible impact on UE-sided CSI prediction performance, concluding that strict consistency between training and inference for these parameters is unnecessary. The document proposes…
CATT concludes that consistency between training and inference regarding antenna down tilt and TXRU mapping is not required, citing simulation results showing negligible performance impact on SGCS. They propose introducing a new processing…
Discussion on AI/ML for beam management
China Telecom submits 16 proposals addressing AI/ML beam management for NR, covering data collection, model inference, performance monitoring, and consistency mechanisms for both network-side and UE-side models. The document focuses on reducing signaling overhead for NW-sided…
China Telecom supports collecting L1-RSRP, beam indices, and timestamps for NW-sided models, while proposing payload reduction via bitmap indications and larger quantization steps for differential RSRP. For UE-sided models, they oppose…
Discussion on specification support for beam management
CMCC presents 55 proposals and 4 observations regarding the specification impact of AI/ML-based beam management in NR, covering data collection, inference, and monitoring for both NW-side and UE-side models. The document addresses critical aspects such as Set A/B configuration,…
CMCC proposes supporting Type 1 and Type 3 data collection contents for NW-side training, emphasizing flexibility for classification and regression models. They require that RS associated with TCI state indication must be measured at least…
Discussion on specification support for positioning accuracy enhancement
CMCC discusses specification impacts for AI/ML-based positioning in NR, focusing on measurement types, data collection, and model monitoring. The document contains 20 proposals and 13 observations covering sample-based vs. path-based measurements, ground-truth label acquisition,…
CMCC slightly prefers sample-based measurements for Case 2b, arguing that they are closer to UE implementation and avoid errors from intermediate path-based processing. They propose supporting candidate values for Nt' such as [9 16 24] or…
Discussion on AI/ML for CSI prediction
CMCC discusses specification impacts for AI/ML-based CSI prediction in Rel-19, focusing on training/inference consistency, performance monitoring, data collection, and inference parameters. The document presents five proposals, arguing against specifying NW-side condition…
CMCC prefers not to specify consistency of training/inference regarding NW-side additional conditions (such as tilt angle and TXRU mapping) unless sufficient simulation results demonstrate non-negligible degradation on model generalization…
Discussion on CSI Prediction
TCL presents 13 proposals regarding AI-based CSI prediction using UE-side models, covering data collection, model inference, and performance monitoring. The document argues for UE-initiated data collection for training, NW-indicated collection for inference and monitoring, and…
TCL proposes that data collection for model training should be requested by the UE, citing that the UE possesses more model information than the network, while data collection for inference and performance monitoring should be indicated by…
Specification support for beam management
This document from vivo addresses specification support for AI/ML-based beam management in NR, focusing on consistency issues for UE-side models, performance monitoring procedures, and reference signal configurations. It contains 62 proposals and 6 observations covering topics…
Vivo proposes that the Associated ID be mandatorily configured in both the inference parameter set and the CSI framework to resolve consistency issues between training and inference phases. They require the introduction of a Pattern ID for…
Specification support for positioning accuracy enhancement
vivo presents a comprehensive contribution for AI/ML-based positioning in NR, focusing on data collection, model inference, and monitoring for Cases 1, 2a, 2b, 3a, and 3b. The document contains 36 proposals and 9 observations, advocating for the extension of agreements from 1st…
vivo proposes extending agreements from 1st priority cases to 2nd priority cases, specifically supporting sample-based channel measurements for Case 2b. They require reusing existing IEs for quality indicators, opposing the definition of…
Specification support for CSI prediction
This document from vivo analyzes the impact of TXRU virtualization mapping mismatches on AI-based CSI prediction generalization, demonstrating significant performance losses in high-speed and outdoor scenarios. It proposes using associated IDs to ensure training-inference…
Vivo identifies TXRU virtualization mapping as a critical NW-side additional condition that causes significant generalization performance loss, particularly at 60km/h or with high proportions of outdoor users. They propose adopting the…
Specification Support for AI/ML for Beam Management
Kyocera submits 25 proposals and 4 observations regarding AI/ML beam management for NR Rel-19, focusing on the configuration of Sets A and B, inference reporting formats, and performance monitoring mechanisms. The document addresses consistency requirements via associated IDs,…
Kyocera proposes distinct configuration strategies for UE-side and NW-side AI/ML models, specifically requiring Set B to be explicitly configured for UE measurements while allowing Set A to be virtually configured for reference mapping.…
AI/ML for beam management
Ericsson presents 21 proposals and 6 observations for AI/ML beam management in NR, focusing on UE-sided model configuration, inference reporting, performance monitoring, and NW-sided data collection overhead reduction. The document argues for configuring associated IDs at the…
Ericsson proposes configuring the associated ID at the CSI-ResourceSet level rather than the CSI-ReportConfig level to preserve fundamental CSI framework assumptions and enable predictions beyond current resourceSet size limits. They…
Specification support for beam management
Tejas Networks Limited presents 39 proposals and 6 observations addressing AI/ML beam management for NR Air Interface, focusing on consistency mechanisms for UE-sided models, performance monitoring metrics, and reporting configurations for both UE and NW-sided models. The…
Tejas Networks proposes that the Associated ID for UE-sided models be configured within the CSI-Report Config to ensure consistency between training and inference phases for Set A and Set B resources. They present a technical case for a…
Specification support for positioning accuracy enhancement
Tejas Networks Limited presents 24 proposals and 15 observations regarding AI/ML for NR positioning accuracy, focusing on sample-based and path-based measurement inputs, model output definitions for Case-3a, training data collection frameworks, and model performance monitoring.…
Tejas Networks proposes specific parameter sets for sample-based measurements, including Nt values of {32, 64, 128} and Nt' as a fraction of Nt, to balance positioning accuracy and reporting overhead. They require redefining the LoS/NLoS…
Specification support for CSI Prediction
Tejas Networks discusses AI/ML-based CSI prediction for Rel-19, focusing on consistency between training and inference, data collection mechanisms, and performance monitoring. The document presents 16 proposals and 3 observations addressing issues such as interference…
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…
On specification for AI/ML-based beam management
OPPO presents a comprehensive set of proposals for AI/ML-based beam management in NR Rel-19, covering both NW-side and UE-side models for BM-Case 1 and BM-Case 2. The document contains approximately 35 distinct proposals and observations, focusing on specification impacts for…
OPPO proposes clarifying beam information as CRI/SSBRI indices for NW-side models and supports implicit temporal reporting for BM-Case2 to reduce overhead. They argue that UE-side additional conditions on Rx beamforming are unnecessary for…
On specification for AI/ML-based positioning accuracy enhancements
OPPO submits 33 proposals and 3 observations regarding specification impacts for AI/ML-based positioning accuracy enhancements in Rel-19, covering measurement enhancements, training/inference consistency, data collection, model inference, and performance monitoring. The document…
OPPO opposes supporting reporting based on phase information for Rel-19 AI-based positioning, arguing that timing and power information are sufficient and phase adds unjustified overhead. They propose using an 'associated ID' signaled from…
On specification for AI/ML-based CSI prediction
OPPO presents 13 proposals and 2 observations regarding AI/ML-based CSI prediction, arguing that UE-sided models face no consistency issues and prioritizing UE-side data collection over NW-side model transfer. The document proposes reusing Rel-18 CSI frameworks for data…
OPPO argues that there is no consistency issue for UE-sided CSI prediction, rendering the associated ID framework unnecessary. They propose prioritizing UE-side data collection because it avoids the complexity of model transfer and high…