Company
vivo
12 contributions across 1 work items
12
Tdocs
1
Work items
Recent position changes
AI-synthesized from contributions · all text is paraphrased
New positions this meeting
- 9.1.1 — Proposes mandatory Associated ID in inference parameter set and CSI framework, Pattern ID for Set B, cell indicator for Associated ID, quasi-best Rx beams, and TRI/differential quantization for overhead reduction.
- 9.1.2 — Supports sample-based channel measurements for Case 2b, proposing specific candidate sets for Nt and k. Requires reusing existing IEs for quality indicators and supports using phase information to enhance positioning accuracy.
Recent contributions
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…
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…
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…
Draft reply LS on applicable functionality reporting for beam management UE-sided model
This document is a Liaison Statement from RAN1 to RAN2 regarding the applicable functionality reporting for UE-sided AI/ML models in beam management for Release 19. It provides technical answers to ten specific questions from RAN2…
Discussion on LS on applicable functionality reporting for beam management UE-sided model
vivo analyzes three options for applicable functionality reporting for AI/ML beam management UE-sided models, arguing that Option 1 is inefficient due to signaling waste and CSI framework conflicts. The document prioritizes Option 2 and…
Specification support for beam management
This document from vivo addresses specification support for AI/ML-based beam management in NR, focusing on consistency issues, performance monitoring, and reporting overhead reduction. It contains 53 proposals and 8 observations covering…
Specification support for positioning accuracy enhancement
This document from vivo analyzes specification impacts for AI/ML-based positioning in NR, focusing on data collection, model inference, and consistency between training and inference. It presents simulation results demonstrating the…
Study on consistency issue for CSI prediction
This document analyzes the impact of TXRU virtualization mapping mismatches on CSI prediction generalization performance, identifying significant SGCS losses under high-speed and outdoor user conditions. It contains 3 observations…
Discussion on CSI compression
This document from vivo analyzes inter-vendor training collaboration for AI/ML-based CSI compression in NR, covering Directions A (UE-side offline engineering), B (NW-side encoder sharing), and C (standardized reference models). It…
Other aspects of AI/ML model and data
This document from vivo analyzes model identification and transfer mechanisms for NR AI/ML, specifically focusing on the feasibility of Case z4 (known model structure transfer). It presents 22 proposals and 7 observations covering…
Study on consistency issue for CSI prediction
Vivo presents a study on training-inference consistency issues for AI/ML-based CSI prediction in NR, analyzing how TXRU virtualization mapping mismatches cause significant performance degradation (up to 44.4% loss). The document contains 2…
Draft reply LS on applicable functionality reporting for beam management UE-sided model
This is a reply liaison statement from vivo's RAN1 to RAN2 providing detailed answers to 10 questions about applicable functionality reporting for beam management UE-sided AI/ML models. The document contains technical clarifications but no…