用众包数据建模5G用户吞吐量，首次实现带置信区间的可解释预测。
Characterizing 5G User Throughput via Uncertainty Modeling and Crowdsourced Measurements
- 基于用户设备众包数据，融合端到端与无线层特征建模吞吐量
- 在5G NSA/SA上提升预测精度8.7%，首次提供5G众包数据基准
- 引入NGBoost输出置信区间，揭示传输与服务层成新瓶颈
下一代网络中应用层用户吞吐量的表征日益困难，因5G无线接入网（RAN）容量提升，连接瓶颈向网络深层转移。传统方法如路测和运营商设备计数成本高、覆盖有限，难以捕捉端到端（E2E）服务质量及其波动性。本文利用大规模众包测量数据——包括由用户设备（UE）收集的端到端、无线、上下文及网络部署特征——提出一种考虑不确定性的可解释下行用户吞吐量估计方法。我们首先验证并改进了既有4G方法，使决定系数R²提升8.7%；随后将其扩展至5G NSA与5G SA场景，首次为5G众包数据集建立基准。为应对吞吐量波动，采用NGBoost模型，首次在计算机通信领域输出点估计与校准置信区间。最后，利用该模型分析从4G到5G SA的演进，发现吞吐量瓶颈从无线接入网移至传输与服务层，端到端指标重要性超越无线相关特征。
原文摘要 · Abstract (English)
Characterizing application-layer user throughput in next-generation networks is increasingly challenging as the higher capacity of the 5G Radio Access Network (RAN) shifts connectivity bottlenecks towards deeper parts of the network. Traditional methods, such as drive tests and operator equipment counters, are costly, limited, or fail to capture end-to-end (E2E) Quality of Service (QoS) and its variability. In this work, we leverage large-scale crowdsourced measurements-including E2E, radio, contextual and network deployment features collected by the user equipment (UE)-to propose an uncertainty-aware and explainable approach for downlink user throughput estimation. We first validate prior 4G methods, improving R^2 by 8.7%, and then extend them to 5G NSA and 5G SA, providing the first benchmarks for 5G crowdsourced datasets. To address the variability of throughput, we apply NGBoost, a model that outputs both point estimates and calibrated confidence intervals, representing its first use in the field of computer communications. Finally, we use the proposed model to analyze the evolution from 4G to 5G SA, and show that throughput bottlenecks move from the RAN to transport and service layers, as seen by E2E metrics gaining importance over radio-related features.
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