R1-2601838 discussion

On beam management for downlink and uplink in 6GR

From Nokia
Status: not treated
WI: FS_6G_Radio
Agenda: 10.5.2.4
Release: Rel-20
Source: 3gpp.org ↗
Nokia's prior position on 10.5.2.4 at RAN1#124 · AI-synthesized, paraphrased
verify sources →
Proposes that NR Release-17 unified TCI framework principles serve as the 6G baseline while studying enhancements to address identified limitations including the limited number of indicated TCI states, reliance on periodic TRS as QCL source RS, and lack of TCI-state specific physical-layer parameter configuration. Proposes a unified design for UE-initiated L1 measurement reporting framework across beam management and beam-based cell switch procedures, citing fragmentation between RAN1-led UEIBR (UCI-based) and RAN2-led LTM event-triggered reporting (MAC CE-based) as a key lesson from 5G NR. Argues that the term beam is non-descriptive and potentially misleading for specifications and should be replaced with procedures reflecting the involved reference signals. Proposes studying CBRA-based BFR with MAC CE signaling as the baseline while also studying streamlined CFRA-based BFR and UL beam failure recovery within a unified TCI state framework. Presents simulation results for inter-cell beam prediction showing Top-1 beam ID accuracy exceeding 90% and Top-2 approaching 99%, and proposes studying cross-frequency beam prediction using one-sided AI/ML models for collocated deployments predicting beams across different component carriers within the same frequency range.

Summary

Nokia's Tdoc R1-2601838 proposes a comprehensive study for 6G Radio Layer 1 beam management, presenting 21 proposals and 16 observations. The document addresses the QCL/TCI framework, downlink and uplink beam measurement/reporting with AI/ML enhancements, beam indication, beam failure recovery, and evaluation methodology.

Position

Nokia proposes a unified 6GR beam management framework based on 5G NR Rel-17 unified TCI principles, while studying enhancements to address identified limitations. They propose studying a more flexible QCL parameterization that allows per-RS configuration of channel properties instead of fixed QCL types, and reducing reliance on periodic TRS as the main QCL source to improve energy efficiency. For AI/ML beam prediction, they position 5G NR Rel-19 spatial domain (BM-case1) and temporal domain (BM-case2) DL Tx beam prediction as the starting point, and propose direct extensions for inter-cell/multi-TRP and cross-frequency beam prediction. They propose studying UE-initiated beam reporting based on 5G NR Rel-19 Event-2 and Event-7, and studying BFR-type signaling as a fallback mechanism, with CBRA-based BFR using MAC CE signaling as the baseline. For evaluation, they provide simulation results showing inter-cell beam prediction achieving over 90% Top-1 beam ID accuracy and cross-frequency beam prediction achieving 2-3 dB mean RSRP error for UEs below -80 dBm.

Key proposals

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