用实测信道数据微调，5G上行性能提升显著。
How Critical is Site-Specific RAN Optimization? 5G Open-RAN Uplink Air Interface Performance Test and Optimization from Macro-Cell CIR Data
- 先用标准模型预训练，再用实地测量数据微调神经接收机。
- 微调后10%误块率所需信噪比降低1.85 dB。
- 证明了站点专属优化对5G空口性能有实质提升，适合网络优化工程师参考。
本文研究特定站点的信道测量数据对空口优化与测试的重要性。当前广泛使用3GPP 38.901中的分段延迟线（TDL）、簇延迟线（CDL）、城市微小区（UMi）和城市宏小区（UMa）等统计信道模型进行空口性能测试与仿真。然而，这些模型在真实场景下的表现仍存在与实测信道数据之间的差距。为弥合这一差距，本文对比了使用统计3GPP TDL模型与实测宏站信道脉冲响应（CIR）数据训练神经接收机的性能差异。我们采用OmniPHY-5G神经接收机进行NR PUSCH上行链路仿真，训练流程包括：先以统计TDL模型预训练，再基于实测站点级MIMO CIR数据进行微调。结果表明，相比仅依赖模拟TDL通道预训练，微调方法在1.85 dB更低的信噪比下即可达到10%误块率（BLER），首次量化回答了‘站点专属微调能带来多大改善’的问题。
原文摘要 · Abstract (English)
In this paper, we consider the importance of channel measurement data from specific sites and its impact on air interface optimization and test. Currently, a range of statistical channel models including 3GPP 38.901 tapped delay line (TDL), clustered delay line (CDL), urban microcells (UMi) and urban macrocells (UMa) type channels are widely used for air interface performance testing and simulation. However, there remains a gap in the realism of these models for air interface testing and optimization when compared with real world measurement based channels. To address this gap, we compare the performance impacts of training neural receivers with 1) statistical 3GPP TDL models, and 2) measured macro-cell channel impulse response (CIR) data. We leverage our OmniPHY-5G neural receiver for NR PUSCH uplink simulation, with a training procedure that uses statistical TDL channel models for pre-training, and fine-tuning based on measured site specific MIMO CIR data. The proposed fine-tuning method achieves a 10% block error rate (BLER) at a 1.85 dB lower signal-to-noise ratio (SNR) compared to pre-training only on simulated TDL channels, illustrating a rough magnitude of the gap that can be closed by site-specific training, and gives the first answer to the question "how much can fine-tuning the RAN for site-specific channels help?"
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