通过不确定性建模和众包测量表征5G用户吞吐量
Characterizing 5G User Throughput via Uncertainty Modeling and Crowdsourced Measurements
AI总结：
本研究利用众包测量和不确定性建模（NGBoost）提出可解释的5G下行吞吐量估计方法，验证4G方法并首次建立5G NSA/SA基准，揭示瓶颈从RAN向传输和服务层转移。
摘要：
在下一代网络中表征应用层用户吞吐量日益具有挑战性，因为5G无线接入网（RAN）的更高容量将连接瓶颈转移到了网络的更深层部分。传统方法，如路测和运营商设备计数器，成本高昂、覆盖有限，或无法捕获端到端（E2E）服务质量（QoS）及其变异性。在这项工作中，我们利用大规模众包测量——包括由用户设备（UE）收集的E2E、无线、上下文和网络部署特征——提出了一种不确定性感知且可解释的下行链路用户吞吐量估计方法。我们首先验证了先前的4G方法，将R²提高了8.7%，然后将其扩展到5G非独立组网（NSA）和5G独立组网（SA），为5G众包数据集提供了首个基准。为了解决吞吐量的变异性，我们应用了NGBoost，这是一种同时输出点估计和校准置信区间的模型，这代表了其在计算机通信领域的首次使用。最后，我们使用所提出的模型分析了从4G到5G SA的演进，并表明吞吐量瓶颈从RAN转移到传输和服务层，这体现在E2E指标相对于无线相关特征的重要性增加上。
英文摘要：
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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