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Discussion on specification support for AI/ML-based beam management
This Futurewei document presents 8 proposals for specification support of AI/ML-based beam management in NR Release 19, covering performance monitoring, model inference, data collection, and assistance information aspects. The company advocates for reusing existing CSI…
Futurewei strongly advocates for reusing existing CSI frameworks as much as possible to minimize specification effort and complexity. They oppose introducing new complex metrics like probability information and confidence levels (rejecting…
Discussion of additional study on AI/ML for NR air interface for CSI compression
Futurewei contributes to AI/ML for NR air interface CSI compression, discussing inter-vendor training collaboration options and providing temporal-domain CSI compression evaluation considering UCI loss. The document contains 10 proposals and 7 observations covering collaboration…
Futurewei advocates FOR using Rel-16 eType II codebook with new/enhanced parameters for data collection and monitoring to achieve better performance, even with additional overhead concerns. They push FOR comprehensive analysis of…
Discussion on other aspects of AI/ML model and data on AI/ML for NR air-interface
FUTUREWEI presents a comprehensive analysis of AI/ML model identification and data management for NR air interface, covering four model identification options, model transfer/delivery mechanisms, and inter-vendor collaboration approaches. The document contains 14 proposals and 3…
FUTUREWEI advocates for simplified model identification approaches that minimize complexity while supporting two-sided models. They strongly push FOR network-controlled model ID assignment, cell-scoped associated IDs, and deprioritizing…
AI/ML for Positioning Accuracy Enhancement
This Ericsson document presents a comprehensive analysis of AI/ML for positioning accuracy enhancement, providing 66 technical proposals across protocol integration, signaling enhancements, model outputs, training data collection, performance monitoring, and model inference. The…
Ericsson strongly advocates for sample-based measurements over legacy path-based measurements, demonstrating superior performance with lower complexity and signaling overhead. They firmly oppose including phase information as model input…
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 reporting for both network-side and UE-side…
Huawei advocates FOR pragmatic reuse of existing CSI framework and signaling mechanisms while pushing AGAINST overly complex new procedures. They strongly support expanding beam measurement capabilities (up to 256 beams vs legacy 64) and…
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 positioning cases (Case 1: UE-based, Case 2:…
Huawei advocates for pragmatic implementation-based solutions over rigid standardization, arguing that ambiguity issues can be resolved through consistent vendor implementations rather than tight specifications. They strongly oppose…
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 5 observations addressing feasibility,…
Huawei strongly advocates AGAINST introducing associated IDs or network-side indications for CSI prediction consistency, arguing they are unnecessary, technically infeasible, and create privacy risks. They push FOR UE-side performance…
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 10 observations covering training…
Huawei advocates for Direction A (dataset/model sharing for UE-side offline engineering) over Direction B (direct parameter sharing) due to significantly lower air-interface overhead, supports prioritizing Option 4 (dataset sharing) and…
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 multiple sections covering different model…
Huawei advocates FOR focusing model identification discussions exclusively on two-sided models while eliminating MI-Option 1 from scope, and pushes FOR using Case y as baseline for UE-side training scenarios. They are AGAINST pursuing Case…
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, configuration content, and activation mechanisms.
Spreadtrum advocates FOR mandatory network-side additional conditions and structured configuration frameworks, pushing for sub-use case level granularity and comprehensive inference configuration requirements. They are positioned AGAINST…
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 and network-side AI/ML models for…
Spreadtrum advocates for UE-initiated data collection for UE-side models, opposes larger quantization steps for inference to maintain accuracy, supports up to 16 beams reporting per instance, and favors reusing existing CSI frameworks to…
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 and configuring associated IDs within the…
Spreadtrum advocates FOR reusing existing AI-BM (beam management) conclusions and associated ID mechanisms for CSI prediction to minimize workload, and pushes AGAINST performance monitoring-based approaches (option 2) which would cause…
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 determination, historical CSI handling,…
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…
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 addressing deprioritization of certain model…
Spreadtrum advocates FOR deprioritizing model transfer cases z1 and z2 in Rel-19 due to cross-vendor collaboration burdens and proprietary design disclosure risks. They support mechanism 1a for UE data collection to avoid privacy exposure…
Discussion on AI/ML for beam management
China Telecom presents a comprehensive technical contribution on AI/ML for NR beam management, covering lifecycle management (LCM) aspects including data collection, model inference, and performance monitoring for both network-sided and UE-sided AI/ML models. The document…
China Telecom advocates for a comprehensive AI/ML beam management framework that supports both network-sided and UE-sided models with flexible data collection mechanisms. They push FOR beam prediction accuracy KPIs as the primary…
Specification support for beam management
This 3GPP RAN1 technical document from Tejas Networks presents 33 proposals and 6 observations for AI/ML-based beam management in 5G NR, covering both UE-sided and network-sided models for spatial (BM-Case1) and temporal (BM-Case2) beam prediction. The document addresses key…
Tejas Networks advocates FOR simplified beam management through single resource set configurations where possible (Set B only for UE-sided BM-Case1), differential L1-RSRP reporting to reduce overhead, and flexible Associated ID mechanisms…
Specification support for positioning accuracy enhancement
This Tejas Networks document addresses AI/ML enhancements for NR positioning accuracy with 15 formal proposals and 14 observations covering model input parameters, training data collection, and performance monitoring across various positioning use cases.
Tejas Networks advocates FOR leveraging existing Release-17 path-based measurement frameworks and emphasizes the critical need to address receiver implementation dependencies in AI-ML model performance. They push AGAINST overly complex new…
Other aspects of AI/ML model and data
This 3GPP RAN1 document from Tejas Networks discusses AI/ML model identification and life cycle management for NR air interface, focusing on associated IDs for ensuring consistency between training and inference phases. The document contains 6 proposals and 4 observations…
Tejas Networks advocates for UE-driven model ID assignment and supports maintaining current model transfer/delivery case prioritizations. They push FOR flexible many-to-one mapping between associated IDs and model IDs to enable generalized…
Discussion and reply LS on applicable functionality reporting for beam management UE-sided model
ZTE provides a comprehensive response to RAN2's liaison statement on AI/ML beam management functionality reporting, addressing 10 specific questions with 14 detailed proposals covering UE capability signaling, network-side conditions, and activation procedures.
ZTE advocates FOR flexible and optional network-side signaling to minimize overhead while maintaining functionality, supporting sub-use-case level granularity for AI/ML beam management, and reusing existing CSI framework mechanisms. They…
Discussion on AI/ML-based beam management
ZTE presents a comprehensive technical document on AI/ML-based beam management for 5G NR with approximately 35 proposals and 3 observations covering functionality-based lifecycle management, data collection enhancements, model inference mechanisms, performance monitoring, and…
ZTE strongly advocates FOR functionality-based lifecycle management over model-ID-based approaches, arguing it reduces complexity and leverages existing UE capability frameworks. They push FOR bitmap-based beam reporting methods to…
Discussion on AI/ML-based positioning enhancement
ZTE presents a comprehensive technical document on AI/ML-based positioning enhancement for NR air interface with 30 proposals and 8 observations covering model training, inference, monitoring, and data collection aspects for different use cases.
ZTE advocates FOR maximizing reuse of existing 3GPP procedures and specifications rather than defining new enhancements, strongly supports CIR with phase information over PDP for better positioning accuracy despite higher overhead, pushes…
Discussion on specification support for AI CSI prediction
ZTE proposes a methodical approach to AI CSI prediction consistency issues, advocating to first identify potential additional conditions before developing detailed solutions. The document contains 2 proposals focused on leveraging existing AI beam prediction mechanisms as a…
ZTE advocates FOR a systematic approach that prioritizes identifying additional conditions before rushing into detailed consistency solutions, and FOR reusing proven AI beam prediction mechanisms. They are AGAINST introducing new…
Discussion on study for AI/ML CSI compression
ZTE provides comprehensive analysis on AI/ML CSI compression inter-vendor collaboration approaches, presenting 27 proposals across three main directions (UE-side offline engineering, on-device operation, fully standardized models) and addressing remaining specification issues…
ZTE advocates for prioritizing Option 3 (standardized reference model structure + parameter exchange) with NW-first training and over-the-air delivery as the most feasible inter-vendor collaboration approach. They push against Option 4 due…
Discussion on other aspects of AI/ML model and data
ZTE's contribution analyzes AI/ML model identification and transfer options for NR air interface, comparing dataset transfer (MI-Option 2), model transfer (MI-Option 3), and standardized reference models (MI-Option 4). The document contains 12 proposals and 5 observations across…
ZTE strongly advocates AGAINST MI-Option 2 (dataset transfer) due to feasibility concerns including huge resource overhead (1-10GB datasets vs 7-100MB models), large latency, and high UE power consumption. They strongly support MI-Option 3…