Due to the large coverage of 5G NB-IoT networks, a more realistic mobility model for a macroscopic scene will greatly facilitate the development of optimal radio resource management algorithms. However, models devised for a random motion scene are no longer applicable in circumstances. Therefore, in this paper, a city-level mobility model is proposed based on the feature mining of the real trajectory of vehicles in the city of Shenzhen. The proposed model is separately designed in the motion trajectory to reduce the mutual influence between the time and spatial sequence. Simulation results show that it can better present specific node motions with the physical constraints of the city layout, which are motivated with a high degree of fit in terms of self-similarity, hotspots, and long-tail features.

中文翻译：

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基于轨迹挖掘的5G NB-IoT网络城市级移动性模型

由于5G NB-IoT网络的覆盖范围很大，因此针对宏观场景的更现实的移动性模型将极大地促进最佳无线电资源管理算法的开发。但是，为随机运动场景设计的模型不再适用于环境。因此，本文基于深圳城市车辆真实轨迹的特征挖掘，提出了城市层次的出行模型。所提出的模型是在运动轨迹中单独设计的，以减少时间和空间序列之间的相互影响。仿真结果表明，它可以更好地呈现具有城市布局物理约束的特定节点运动，这些运动在自相似性，热点和长尾特征方面具有很高的契合度。