Lumos5G Dataset

The emerging 5G services offer numerous new opportunities for networked applications. In this study, we seek to answer two key questions: i) is the throughput of mmWave 5G predictable, and ii) can we build "good" machine learning models for 5G throughput prediction? To this end, we conduct a measurement study of commercial mmWave 5G services in a major U.S. city, focusing on the throughput as perceived by applications running on user equipment (UE). Through extensive experiments and statistical analysis, we identify key UE-side factors that affect 5G performance and quantify to what extent the 5G throughput can be predicted. We then propose Lumos5G - a composable machine learning (ML) framework that judiciously considers features and their combinations, and apply state-of-the-art ML techniques for making context-aware 5G throughput predictions. We demonstrate that our framework is able to achieve 1.37x to 4.84x reduction in prediction error compared to existing models. Our work can be viewed as a feasibility study for building what we envisage as a dynamic 5G throughput map (akin to Google traffic map). We believe this approach provides opportunities and challenges in building future 5G-aware apps.

新兴的5G服务为网络化应用带来了诸多全新机遇。本研究旨在解答两个核心问题：其一，毫米波（mmWave）5G的吞吐量是否具备可预测性？其二，我们能否构建出性能优异的机器学习模型用于5G吞吐量预测？为此，我们在美国一座大城市开展了商用毫米波5G服务的测量研究，重点关注用户设备（User Equipment，UE）上运行的应用所感知到的吞吐量。通过大规模实验与统计分析，我们明确了影响5G性能的关键UE侧因素，并量化了5G吞吐量可被预测的程度。随后，我们提出了Lumos5G——一种可组合式机器学习（ML）框架，该框架审慎考量特征及其组合方式，并应用前沿机器学习技术实现上下文感知的5G吞吐量预测。我们证明，相较于现有模型，该框架可将预测误差降低1.37倍至4.84倍。本研究可被视为构建我们所设想的动态5G吞吐量地图（类似谷歌交通地图）的可行性研究。我们认为，该方法为构建未来具备5G感知能力的应用提供了机遇与挑战。

搜集汇总

背景与挑战

背景概述

Lumos5G数据集是一个关于商业毫米波5G网络性能测量的开放数据集，包含用户设备在移动过程中收集的信号强度、位置、速度等参数及吞吐量数据，用于机器学习模型训练和5G吞吐量预测研究。数据集覆盖了美国明尼阿波利斯市中心一个1300米的环形区域，包含步行和驾驶两种移动模式下的测量数据。