Company
ZTE
17 contributions across 2 work items
17
Tdocs
2
Work items
Recent position changes
AI-synthesized from contributions · all text is paraphrased
New positions this meeting
- 9.1.1 — Proposes functionality-based LCM without model ID, bitmap-based beam reporting, L1 signaling for NW data collection, and optional associated ID configuration for NW-side conditions.
- 9.1.2 — Proposes reusing existing legacy signaling structures for timestamps and quality indicators. Supports inclusion of phase information (CIR) as model input, presenting technical evidence of superior accuracy. Opposes reporting transmit offset from gNB to LMF in Case 3b.
- 9.1.3 — Argues against treating tilt/TXRU as NW-side conditions, proposes reusing Rel-18 MIMO CSI prediction codebook, and supports Type 2/3 monitoring.
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9.1.2 strengthenedZTE strengthened their position by providing specific quantitative evidence (1.2-2.2x performance improvement) to support their advocacy for CIR with phase information.
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9.1.3 strengthenedZTE evolved from a systematic approach with conditional support to a stronger opposition stance, now emphasizing model generalization capabilities as sufficient without additional consistency mechanisms.
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9.1.4.2 strengthenedZTE provided more specific technical justification for opposing MI-Option 2 with concrete resource overhead figures (1-10GB transfers) and added explicit support for Case z4.
Recent contributions
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…
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…
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.…
[Draft] Reply LS on LMF-based AI/ML Positioning for Case 2b
This document is a Liaison Statement reply from RAN1 to SA2 regarding LMF-based AI/ML Positioning for Case 2b in Release 19. It outlines current agreements on data types (timing, power, phase) and measurement alternatives (sample-based vs.…
[Draft] Reply LS on LMF-based AI/ML Positioning for Case 3b
This document is a Liaison Statement reply from RAN1 to SA2 regarding LMF-based AI/ML positioning for Case 3b in Rel-19. It outlines current progress and future work plans for data types and procedures, detailing agreements on channel…
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,…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…