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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 include supporting larger beam sets (up to…
Huawei proposes studying mechanisms to support beam sets exceeding 64 resources, either through multiple legacy sets or a single set up to 256 resources. They require the associated ID for UE-side models to be limited to a cell-specific…
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 implementation flexibility, reuse of legacy…
Huawei argues that ambiguity in sample-based measurements for Case 3b can be avoided by implementation rather than strict specification, proposing that gNBs flexibly determine selection window parameters (Nt, Nt', k) while capping Nt' at…
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 observations covering training methods, overhead…
Huawei argues that for Direction A inter-vendor collaboration, sharing datasets (Option 4-1) incurs less proprietary risk than sharing model parameters (Option 3a-1/3b). They propose that model backbone information is unnecessary for…
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 and 3), argues for Case y as the…
Huawei argues that MI-Option 1 is inapplicable to the revised WID scope for two-sided models and proposes excluding it from further discussion. For MI-Option 2 and 3, Huawei specifies that model identification relies on the delivery of…
AI/ML for Positioning Accuracy Enhancement
Ericsson presents a comprehensive technical case for Rel-19 AI/ML-based positioning, strongly favoring sample-based measurements over legacy path-based reporting due to lower complexity and better generalization across different channel estimators. The document contains 73…
Ericsson argues inapplicability of Rel-18 carrier phase positioning for AI/ML inputs, proposing to down-prioritize CIR model inputs due to high signaling overhead and difficulty aligning phase measurements between training and inference.…
Discussion on AI/ML for Beam Management
Quectel presents 15 proposals regarding AI/ML-based beam management in NR, focusing on data collection frameworks for network and UE-side models, inference reporting configurations, and performance monitoring mechanisms. The document addresses specification impacts for Beam…
Quectel proposes extending UE capability for RS measurement beyond 64 RS per resource set to support larger Set A configurations, utilizing bitmaps for Set B pattern selection. They support configuring two resource sets for Set A and Set B…
AI/ML for CSI prediction
Ericsson presents evaluation results for UE-sided AI/ML CSI prediction, concluding that no specification enhancements are needed to ensure consistency between training and inference regarding UE speed, deployment scenario, carrier frequency, NW antenna tilt, or TXRU mapping, as…
Ericsson argues inapplicability of the inconsistency issue identified for UE-sided spatial beam prediction to the UE-sided CSI prediction use case, which relies on temporal domain correlation rather than spatial beam sets. They conclude…
AI/ML for CSI compression
Ericsson presents a comprehensive analysis of inter-vendor training collaboration options for AI/ML-based CSI compression, arguing for the use of 3GPP synthetic data and standardized phase normalization to ensure interoperability. The document evaluates three main directions (A,…
Ericsson requires that reference models for inter-vendor collaboration (Options 1, 3a, 3b) be designed using 3GPP channel model based synthetic data rather than field data, citing excessive work and bias risks. They oppose UE-side first…
Discussion on AI/ML for beam management
InterDigital presents 29 proposals and 24 observations regarding AI/ML for beam management in NR, focusing on configuration frameworks, reporting overhead reduction, and lifecycle management. The document argues for Option 2 for UE-side model applicability to minimize signaling…
InterDigital supports Option 2 for UE-side model applicability identification, arguing that Option 1 requires excessive configuration overhead for candidate CSI report configurations. They prefer Alt 2 for beam configuration, utilizing a…
Discussion and reply LS on applicable functionality reporting for beam management UE-sided model
ZTE analyzes the applicable functionality reporting procedures for UE-sided AI/ML beam management models, specifically addressing questions from a RAN2 Letter of Agreement. The document presents 15 distinct proposals across four sections, arguing against Option 1 for inference…
ZTE opposes Option 1 for UE-side model inference applicability, arguing it causes substantial waste of air interface resources and complicates NW scheduling. They support Option 3, emphasizing its compatibility with legacy CSI-ReportConfig…
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-based beam reporting and threshold-based data omission. The document outlines specific enhancements for NW-side data collection, UE-side…
ZTE proposes utilizing functionality-based LCM without model ID based signaling for AI/ML beam management, arguing that model transfer challenges and existing associated ID support diminish the need for model-ID-based approaches. They…
Discussion on AI/ML-based positioning enhancement
ZTE presents a comprehensive contribution on AI/ML-based positioning enhancements for Rel-19, containing 30 proposals and 11 observations across model input, output, training, and monitoring. The document strongly favors sample-based measurements over path-based ones due to…
ZTE proposes supporting sample-based measurements for Rel-19 AI/ML positioning, arguing that implementation ambiguities in path-based measurements cannot be removed, whereas sample-based ambiguities can be resolved via LMF configuration.…
Discussion on specification support for AI CSI prediction
ZTE presents simulation results evaluating the generalization capability of AI-based CSI prediction models across different down tilt angles and TXRU mappings. The document contains two observations regarding model performance and one proposal concluding that neither down tilt…
ZTE argues that down tilt angle and TXRU mapping should not be defined as network-side additional conditions requiring an associated ID for AI CSI prediction. They present technical evidence showing that AI models generalize well across…
Discussion on study for AI/ML CSI compression
ZTE analyzes inter-vendor training collaboration options for AI/ML-based CSI compression in NR Release 19, focusing on Directions A (UE-side offline engineering), B (on-device operation), and C (fully standardized reference model). The document presents 32 proposals and 10…
ZTE proposes conducting comparisons between Case 2 and Case 3 for potential down selection to reduce specification impact analysis efforts, and deferring specification impact analysis for inter-vendor training collaboration until…
Discussion on other aspects of AI/ML model and data
ZTE analyzes model identification options for two-sided AI/ML models in NR, arguing against dataset transfer (MI-Option 2) due to high overhead and latency, while favoring model parameter transfer (MI-Option 3) and standardization of reference models (MI-Option 4). The document…
ZTE presents a technical case against MI-Option 2 (dataset transfer), citing huge resource overhead, large latency, and potential performance degradation due to backbone misalignment. They prefer MI-Option 4 (standardization of reference…
Discussion on LS on applicable functionality reporting for beam management UE-sided model
CMCC analyzes the signaling procedures for applicable functionality reporting in NR AI/ML beam management, specifically addressing Steps 3-5 of the RAN2-defined lifecycle. The document presents 15 proposals and 2 observations, arguing that NW-side additional conditions should be…
CMCC proposes that NW-side additional conditions be functionality specific and optional, arguing that UE can determine applicable functionalities based on inference configuration or parameters provided in Step 3 without mandatory NW-side…
Discussion on specification support for beam management
CMCC presents 49 proposals and 2 observations regarding the specification impacts of AI/ML-based beam management for NR, covering data collection, inference, and monitoring for both NW-side and UE-side models. The document addresses configuration of Set A and Set B, reporting…
CMCC proposes that L1 signaling be supported for NW-sided training data collection and that Top-K beam sweeping be supported for NW-side inference to enhance prediction accuracy. They require that for UE-side models, the overall CPU be…
Discussion on specification support for positioning accuracy enhancement
CMCC analyzes specification impacts for AI/ML-based positioning in NR, presenting 17 proposals and 12 observations across methodology, measurement definitions, data collection, and model monitoring. The document argues for sample-based measurements over path-based ones to reduce…
CMCC slightly prefers sample-based measurements for AI/ML positioning to avoid the potential errors introduced by intermediate path-based processing at the UE. They propose that the entity deriving the AI model should provide the…
Discussion on AI/ML for CSI prediction
CMCC discusses specification impacts for AI/ML-based CSI prediction in Rel-19, focusing on ensuring consistency between training and inference phases. The document presents 8 proposals covering consistency mechanisms via associated IDs and performance monitoring, data collection…
CMCC proposes studying two options for ensuring consistency of NW-side additional conditions across training and inference for AI-based CSI prediction: one based on associated ID and another based on performance monitoring. They propose…
Discussion on AI/ML for CSI compression
CMCC presents evaluation results for AI/ML-based temporal domain CSI compression (Cases 2 and 3), demonstrating significant SGCS gains over Rel-16 and Rel-18 benchmarks. The document proposes prioritizing inter-vendor collaboration Directions A and B, specifies requirements for…
CMCC prioritizes inter-vendor collaboration Directions A and B for further study, arguing they offer better performance and lower specification effort than Direction C. They propose that for Direction A, the NW-side must share dataset…
Discussion on other aspects of AI/ML model and data
CMCC discusses model identification options (MI-Option 2, 3, and 4) and model transfer/delivery cases for NR AI/ML, proposing to prioritize MI-Option 2 and 3 while deprioritizing MI-Option 4 due to specification effort and RAN4 dependencies. The document outlines specific…
CMCC proposes prioritizing MI-Option 2 and MI-Option 3 for model identification while deprioritizing MI-Option 4 due to specification efforts and RAN4 dependencies. For MI-Option 2, they propose studying whether the NW or UE assigns the…
Discussion on AI/ML for positioning accuracy enhancement
This document from H3C proposes specific algorithms for eliminating initial phase mismatch in Channel Impulse Response (CIR) measurements used as AI/ML model inputs for NR positioning. It defines reference sample selection criteria (strongest path, first satisfied sample) and…
H3C supports employing CIR as AI/ML model input for both direct and assistant positioning, arguing it preserves more channel information than PDP or DP. They require the elimination of initial phase mismatch in CIR measurements before…
Specification Support for AI/ML for Beam Management
Kyocera presents a comprehensive framework for AI/ML-assisted beam management in NR Rel-19, covering configuration, inference reporting, consistency, and performance monitoring for both NW-sided and UE-sided models. The document contains 35 proposals and 4 observations, focusing…
Kyocera proposes that for UE-side AI/ML models, Set A be virtually configured for reference mapping while Set B is explicitly configured for measurements, requiring a new IE to associate beams with these sets. They require the introduction…
Discussion for supporting AI/ML based beam management
Samsung presents 30 proposals and 1 observation regarding AI/ML-based beam management for NR, covering both NW-side and UE-side models. The document addresses data collection for training and inference, spatial and temporal enhancements for beam reporting, consistency mechanisms…
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…
Discussion for supporting AI/ML based positioning accuracy enhancement
Samsung presents a comprehensive discussion on AI/ML-based positioning accuracy enhancement, outlining 29 observations across triggering, model selection, data collection, inference, monitoring, and consistency checks. The document emphasizes the need for processed channel…
Samsung argues that full-size raw channel measurements are unsuitable for data collection due to prohibitive overhead and storage costs, proposing instead that truncated or feature-extracted measurements be used. They support explicit…
Views on AI/ML based CSI prediction
Samsung presents observations and proposals for AI/ML-based CSI prediction in NR, highlighting the performance degradation when models are trained on mismatched TRP antenna settings and the need for network assistance to ensure training/inference consistency. The document…
Samsung argues that site-specific AI/ML models significantly outperform generic models, particularly at large prediction horizons, and demonstrates that data distribution mismatch due to different TRP antenna settings (e.g., [2,8,2] vs…
Views on additional study for AI/ML based CSI compression
Samsung presents views on further studies for AI/ML-based CSI compression in Rel-19, focusing on temporal aspects (Case 2 and Case 3), performance-complexity trade-offs, and inter-vendor training collaboration. The document contains 18 proposals and 16 observations, arguing that…
Samsung proposes that angle-delay (W2) domain compression is significantly more robust to data distribution mismatches than spatial-frequency (W) domain compression, citing up to 37.9% degradation for W-domain versus only 0.7% for…
Views on additional study for other aspects of AI/ML model and data
Samsung analyzes model identification and data handling for AI/ML in NR, presenting 13 proposals and 5 observations across model-level management, two-sided model consistency, and data privacy. The document argues that explicit model identification is unnecessary for ensuring…
Samsung argues that explicit model identification is not required to ensure consistency between model training and inference, proposing instead that the indication of associated IDs for network-side additional conditions is sufficient.…
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 frameworks to ensure consistency between…
Spreadtrum supports Option 1 or Option 2 for UE functionality determination, requiring the associated ID to be configured for both training and inference to guarantee consistency. They oppose configuring only Set B resources for UE-side…
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 contains two main proposals: using the…
Spreadtrum argues that the 'associated ID' mechanism, previously introduced for Beam Management (AI-BM), should be reused for CSI prediction to ensure consistency of network-side additional conditions across training and inference. They…