AI 中文总结
本文介绍用于预测资源管理的移动集成网络轨迹数据集MINT-V2X，它通过耦合SUMO与OMNeT++/Simu5G生成，经多项测试验证，包含大量同步数据点，通过案例研究证明其在RSU负载预测上比仅用网络历史基线性能更好。
AI 中文摘要
车联网（V2X）通信系统基于不仅包含车辆轨迹数据，还包含具有现实保真度的无线网络参数的数据集，以创建预测和优化模型。目前存在关键的研究基础设施差距，公开数据集往往局限于移动性或网络参数之一，很少提供两者结合的单一综合视图。本文介绍了MINT-V2X，这是一个通过将SUMO交通动态与OMNeT++/Simu5G网络模拟相结合生成的综合数据集。验证框架由基于3GPP Release 14（C-V2X）、ETSI标准和香农容量理论的14项标准化测试组成。生成的数据集在3小时的城市交通模拟中包含来自15个路边单元（RSU）的1386辆车的987万个同步数据点。通过网络指标相关性（CQI-SINR：0.993；SINR-PDR：0.946）证明了严格的算法一致性。最后，通过进行RSU负载预测案例研究证明了数据集的价值，表明使用轨迹数据比仅基于网络历史的基线具有更好的预测性能。数据集、实验和完整的SUMO配置文件可在GitHub存储库中获取，以方便在替代模拟堆栈上进行再现。
英文摘要
Vehicle-to-Everything (V2X) communication systems are based on datasets that not only contain vehicle trajectory data but also wireless network parameters with a realistic level of fidelity, enabling the creation of prediction and optimization models. There is a very critical research infrastructure gap today, and publicly available datasets are likely to be limited to one of the two: mobility or network parameters, and rarely provide a single, integrated view that combines both. This paper introduces MINT-V2X, a comprehensive dataset generated by coupling SUMO traffic dynamics with OMNeT++/Simu5G network simulation. The validation framework is composed of 14 standardized tests based on 3GPP Release 14 (C-V2X), ETSI standards and Shannon capacity theory. The resulting dataset contains 9.87 million synchronized data points from 1,386 vehicles from 15 roadside units (RSUs) during 3 hours of urban traffic simulation. We demonstrate strict algorithmic consistency through network metric correlations (CQI-SINR: 0.993; SINR-PDR: 0.946). Finally, we demonstrate the value of the dataset by conducting an RSU load prediction case study, showing that using trajectory data yields better predictive performance than network-history-only baselines. The dataset, experiments, and complete SUMO configuration files are available in the GitHub repository to facilitate reproduction on alternative simulation stacks.
