The evolution of wireless communications into 6G and beyond is expected to rely on new machine learning (ML)-based capabilities. These can enable proactive decisions and actions from wireless-network components to sustain quality-of-service (QoS) and user experience. Moreover, new use cases in the area of vehicular and industrial communications will emerge. Specifically in the area of vehicle communication, vehicle-to-everything (V2X) schemes will benefit strongly from such advances. With this in mind, we have conducted a detailed measurement campaign with the purpose of enabling a plethora of diverse ML-based studies. The resulting datasets offer GPS-located wireless measurements across diverse urban environments for both cellular (with two different operators) and sidelink radio access technologies, thus enabling a variety of different studies towards V2X. The datasets are labeled and sampled with a high time resolution. Furthermore, we make the data publicly available with all the necessary information to support the on-boarding of new researchers. We provide an initial analysis of the data showing some of the challenges that ML needs to overcome and the features that ML can leverage, as well as some hints at potential research studies.
翻译:预计无线通信演变为6G及以后将依赖基于新机器学习(ML)的能力,从而能够通过无线网络组件作出积极主动的决定和行动,维持服务质量(QOS)和用户经验;此外,车辆和工业通信领域将出现新的使用案例;具体而言,车辆通信、车辆到普及(V2X)计划领域将大大受益于这些进展;铭记这一点,我们开展了一项详细的测量运动,目的是促成大量基于多元ML的研究;由此产生的数据集为移动电话(与两个不同的操作者)和侧链接无线电接入技术提供了跨不同城市环境的全球定位系统无线测量,从而能够对V2X进行各种研究。数据集的标签和抽样将高分辨率标出。此外,我们公布数据,提供一切必要信息,支持新研究人员登船。我们初步分析了ML需要克服的一些挑战以及ML能够利用的特征,以及潜在研究的一些提示。