论文标题
两者中最好的：混合数据驱动和基于模型的车辆网络模拟
The Best of Both Worlds: Hybrid Data-Driven and Model-Based Vehicular Network Simulation
论文作者
论文摘要
在具体评估场景中对新型移动沟通方法的端到端行为的分析经常导致方法论上的困境：现实世界的测量活动非常耗时，并且缺乏可控的环境，因此分析模型的推导通常无法导致系统级别的复杂性，系统级别的简化导致了实现实际世界的巨大范围，从而实现了实质性的范围。在本文中，我们提出了一种混合模拟方法，该方法将基于模型的移动性仿真，多维无线电环境图（REMS）汇总在一起，以有效地维护无线电传播数据以及数据驱动的网络模拟（DDNS），以快速准确地分析移动网络的端到端行为。为了进行验证，我们分析了机会性的车辆数据传输用例，并将所提出的方法与现实世界测量值以及网络模拟器3（NS-3）中的相应模拟设置进行了比较。与后者相比，所提出的方法不仅能够更好地模仿现实世界的行为，而且还可以提高计算效率的300倍。
The analysis of the end-to-end behavior of novel mobile communication methods in concrete evaluation scenarios frequently results in a methodological dilemma: Real world measurement campaigns are highly time-consuming and lack of a controllable environment, the derivation of analytical models is often not possible due to the immense system complexity, system-level network simulations imply simplifications that result in significant derivations to the real world observations. In this paper, we present a hybrid simulation approach which brings together model-based mobility simulation, multi-dimensional Radio Environmental Maps (REMs) for efficient maintenance of radio propagation data, and Data-driven Network Simulation (DDNS) for fast and accurate analysis of the end-to-end behavior of mobile networks. For the validation, we analyze an opportunistic vehicular data transfer use-case and compare the proposed method to real world measurements and a corresponding simulation setup in Network Simulator 3 (ns-3). In comparison to the latter, the proposed method is not only able to better mimic the real world behavior, it also achieves a 300 times higher computational efficiency.
