Company
Ericsson
16 contributions across 1 work items
16
Tdocs
1
Work items
Recent position changes
AI-synthesized from contributions · all text is paraphrased
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9.1.2 strengthenedEricsson hardened their stance on measurement inputs, moving from a compromise position supporting both sample-based and path-based measurements to explicitly opposing multi-port PDP due to doubled signaling size and marginal performance gains. They consolidated their technical case against phase information and CIR inputs, citing random initial phase issues and high signaling overhead. Additionally, they refined the monitoring framework by establishing self-monitoring as the baseline, with the inference entity responsible for metric calculation, rather than the broader label-free self-monitoring proposal from the prior meeting.
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9.1.3 strengthenedEricsson hardened its opposition to consistency enhancements by consolidating per-aspect arguments into a blanket conclusion of inapplicability. They refined their proposal by specifying the reuse of the functionality-based LCM framework and requiring the typeII-Doppler-r18' codebook format for both predicted and ground-truth CSI. They narrowed the intermediate KPI scope to SGCS only, explicitly excluding NMSE, and maintained their opposition to Type 2 monitoring.
New positions this meeting
- 9.1.3 — Argues against extensive specification enhancements for training/inference consistency, claiming UE-sided CSI prediction models demonstrate sufficient generalization capability across various network conditions.
- 9.1.4.1 — Strongly advocates for Direction C (fully standardized reference models) as the most feasible inter-vendor solution, supporting 3GPP channel model-based synthetic data while opposing over-the-air delivery for Direction A due to complexity concerns.
Recent contributions
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…
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…
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…
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…
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…
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…
Discussion on other aspects of AI/ML
Ericsson analyzes model identification options for two-sided AI/ML models in NR, specifically focusing on CSI compression use cases. The document presents 7 proposals and 6 observations, arguing that over-the-air dataset delivery is…
AI/ML for beam management
Ericsson presents a comprehensive technical document on AI/ML for beam management in NR air interface, containing 20 proposals and 3 observations. The document addresses both UE-sided and NW-sided AI/ML models for beam management…
Summary #1 of specification support for positioning accuracy enhancement
This 3GPP RAN1 meeting summary document (R1-2410714) from Ericsson covers AI/ML for NR Air Interface positioning accuracy enhancement, containing over 90 proposals and conclusions across 8 major sections covering model input, output,…
Summary #2 of specification support for positioning accuracy enhancement
This document contains approximately 15 proposals and 2 conclusions from Ericsson covering AI/ML positioning enhancements including sample-based vs path-based measurements, LOS/NLOS indicators for model output, training data collection…
Summary #3 of specification support for positioning accuracy enhancement
This document is RAN1#119 Summary #3 from Ericsson covering AI/ML positioning accuracy enhancement with approximately 120+ proposals/conclusions across model input, output, training data collection, inference, and monitoring. The document…
Summary #4 of specification support for positioning accuracy enhancement
This 3GPP RAN1 document (Tdoc R1-2410717) from Ericsson summarizes discussions on AI/ML-based positioning accuracy enhancements from RAN1#119, containing over 80 proposals across model input definitions, output specifications, training…
Summary #5 of specification support for positioning accuracy enhancement
This RAN1 document from Ericsson presents 95 proposals across 6 major technical areas for AI/ML-based positioning enhancement in NR, covering model input definitions, model output specifications, training data collection, inference…
Final summary of specification support for positioning accuracy enhancement
This 3GPP RAN1 document (R1-2410921) from Ericsson presents a final summary of discussions on AI/ML for NR Air Interface positioning accuracy enhancement from RAN1#119 meeting. The document contains over 160 proposals and conclusions…
RAN2 inputs to TR 38.843
This is a liaison statement from RAN2 to RAN1 regarding UE-side data collection for UE-side model training in AI/ML for NR Air Interface work. The document contains no technical proposals but rather informs RAN1 that RAN2 has endorsed a…
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,…