R1-2600149
discussion
Transmission schemes for 6GR UL channels
From Huawei
Summary
This Huawei/HiSilicon 3GPP Tdoc proposes physical layer enhancements for 6G Radio (6GR) uplink transmission, covering PUSCH, DMRS, and PUCCH/L1-UCI design. The document makes 34 formal proposals and 26 observations, focusing on UL MIMO codebook improvements for irregular UE antennas, high-order MU-MIMO support (up to ~100 layers), low-overhead scalable DMRS design with AI/ML receiver options, and simplified control channel mechanisms.
Position
Huawei proposes studying enhanced UL codebook designs that address performance losses from irregular UE antenna array layouts, presenting SLS results showing up to 50.3% CA and 51.1% CE spectral efficiency loss for 8Tx/4Tx NR DFT codebooks versus ideal SVD precoding. They propose high-precision UL coherent codebooks achieving 0.3–1.5 dB gains over NR R15 codebooks under improved UE coherency, and present AI/ML-based UL precoder generation with 30% SGCS gain and 10% SE gain over legacy codebooks. The company requires support for high-order MU-MIMO up to ~100 layers at 8T512R for around 7GHz deployments, achieving ~3.5x cell-average SE gains over 5G NR 4T64R, and proposes studying multi-layer DFT-s-OFDM MIMO transmission with codebook and DMRS design to be addressed in both waveform and MIMO agendas. For DMRS, they propose scalable low-overhead DMRS port numbers up to 96 with unified design for UL/DL CP-OFDM, favoring sparse orthogonal DMRS for AI/ML-based overhead reduction across both capacity and coverage scenarios while limiting SIP and DMRS-free solutions to coverage-enhancement scenarios only, and provide link-level evaluations showing 0.3–1.9 dB SNR gains with AI/ML CE/Rx receivers. They argue against moving UCI to L2 signaling, presenting a technical case that L1 UCI provides superior efficiency (avoiding 10–20x overhead increase), lower latency (avoiding retransmission delays), better coding performance with RM/Polar codes versus LDPC, and higher reliability with 1% BLER targets versus 10% for data.
Key proposals
- Proposal 1 (Sec 2.2): 6GR UL MIMO transmission enhancements should be considered to meet the requirements on spectral efficiency and UL coverage, considering support of new frequency bands (around 7GHz), device capabilities evolution and increased BS Massive MIMO dimensioning, and at least focus on enhanced UL transmission schemes, support of larger number of MU-MIMO layers, advanced RS design including low-overhead DMRS, and Low-PAPR design for DFT-s-OFDM based UL multi-layer transmission.
- Proposal 2 (Sec 3.1): UL closed-loop (CB-based and NCB-based schemes) and open-loop uplink transmission schemes should be studied in 6GR.
- Proposal 3 (Sec 3.2): Uplink codebook enhancements for full-coherent, partial-coherent and non-coherent codebooks should be studied in 6GR considering at least high-resolution codebooks, and realistic terminal antenna deployments.
- Proposal 5 (Sec 3.4): Open-loop diversity scheme should be studied for uplink transmission in 6GR.
- Proposal 9 (Sec 5.1): 6GR UL MIMO design should support up to rank 8 for SU-MIMO transmissions.
- Proposal 10 (Sec 5.2): 6GR UL MIMO design should consider the support of high-order MU-MIMO transmission with larger number of transmission layers, e.g., up to ~100 layers.
- Proposal 12 (Sec 7): Study solutions to improve overall system throughput for scenarios with significant SINR imbalance among MIMO layers for 6GR UL MIMO.
- Proposal 14 (Sec 9.1.1): Larger DMRS port number (e.g., up to 96) should be considered for DMRS enhancement in 6GR.
- Proposal 19 (Sec 9.1.6): Enhancement on DMRS channel and interference estimation should be studied, considering low-overhead DMRS and larger spatial multiplexing dimension of MU-MIMO in 6GR.
- Proposal 24 (Sec 9.4.1): For low overhead DMRS with AI/ML receiver, study single-sided model (i.e., NW-sided model for UL) as a starting point for UL transmission.
- Proposal 25 (Sec 9.4.1): For AI/ML based DMRS overhead reduction, the AI/ML based solution can be justified based on whether it can achieve better performance or lower DMRS density than the non-AI/ML based DMRS overhead reduction solution. From evaluation perspective, find a basic non-AI/ML benchmark (e.g., normal/sparse DMRS pattern with traditional receiver) as the common benchmark for both AI/ML solution and enhanced non-AI/ML solution.
- Proposal 28 (Sec 10.1.1): 6GR should keep UCI including at least SR, HARQ-ACK, and CSI as L1 UL control information.
- Proposal 31 (Sec 10.2): 6GR should consider reducing the number of PUCCH formats and study unified PUCCH format for reducing complexity and improving transmission performance.
- Proposal 32 (Sec 10.3): 6GR should study simplified mechanisms for UCI multiplexing on PUCCH and PUSCH.
- Proposal 34 (Sec 11.2): Consider link-level simulation for AI/ML-based DMRS evaluation with KPIs including BLER, spectral efficiency, PAPR, Net gain, and complexity metrics (FLOPs/MACs, number of parameters), along with generalization/scalability performance across deployment scenarios, antenna configurations, spatial/frequency/time domain factors, and imperfect/non-ideal factors.