真实6G移动场景数据集，助力AI优化切换与波束管理。
Enabling AI-Native Mobility in 6G: A Real-World Dataset for Handover, Beam Management, and Timing Advance
- 从商用网络采集多模式、多速度移动数据，真实反映用户行为。
- 包含切换中断时长、时序提前（TA）等关键信号测量值。
- 适合研究AI/ML模型在移动性管理中的推理与性能评估。
为解决高速5G场景下用户设备（UE）移动性带来的高中断时延和测量报告开销问题，已有研究提出利用人工智能/机器学习（AI/ML）技术进行波束管理和移动性控制。然而，这些方法依赖的大多是仿真数据，难以反映真实部署环境中的行为和流量模式。因此，亟需在多种条件下获取真实数据。本文基于商用网络，采集了行人、自行车、汽车、公交车及火车等多种移动模式、多速度下的实际数据，重点聚焦切换（HO）场景，旨在降低切换中断时间并维持切换前后连续吞吐量。数据集还包含多个信令事件（如随机接入触发、MAC CE、PDCCH授权）下的时序提前（TA）测量值，这些在现有研究中常被忽略。本文详细描述了数据集构建过程，包括实验设置、数据采集与提取方法，并进行了探索性分析，重点关注移动性、波束管理与TA特性。此外，讨论了多个应用场景，例如训练和评估各类用于TA预测的AI/ML模型。
原文摘要 · Abstract (English)
To address the issues of high interruption time and measurement report overhead under user equipment (UE) mobility especially in high speed 5G use cases the use of AI/ML techniques (AI/ML beam management and mobility procedures) have been proposed. These techniques rely heavily on data that are most often simulated for various scenarios and do not accurately reflect real deployment behavior or user traffic patterns. Therefore, there is an utmost need for realistic datasets under various conditions. This work presents a dataset collected from a commercially deployed network across various modes of mobility (pedestrian, bike, car, bus, and train) and at multiple speeds to depict real time UE mobility. When collecting the dataset, we focused primarily on handover (HO) scenarios, with the aim of reducing the HO interruption time and maintaining continuous throughput during and immediately after HO execution. To support this research, the dataset includes timing advance (TA) measurements at various signaling events such as RACH trigger, MAC CE, and PDCCH grant which are typically missing in existing works. We cover a detailed description of the creation of the dataset; experimental setup, data acquisition, and extraction. We also cover an exploratory analysis of the data, with a primary focus on mobility, beam management, and TA. We discuss multiple use cases in which the proposed dataset can facilitate understanding of the inference of the AI/ML model. One such use case is to train and evaluate various AI/ML models for TA prediction.
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