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 that paves the way to 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 onboarding 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:随着无线通信进化至第六代及以后,预计将依赖于新的基于机器学习(ML)的能力,这些能力能够实现无线网络组件的主动决策和行动,以维持服务质量(QoS)和用户体验。此外,车载和工业通信领域将出现新的用例。特别是在车辆通信领域,车辆间一切(V2X)方案将非常从这些进步中受益。基于此,我们进行了详细的测量活动,为各种多样化的基于ML的研究铺平了道路。所得到的数据集为两种不同运营商的蜂窝网络和侧链无线电接入技术提供了GPS定位的无线电信号测量数据,从而为V2X的各种不同研究提供了可能。数据集经过标签化处理,具有高时间分辨率的采样。此外,我们公开提供数据,同时提供所有必要的信息,以支持新研究人员的加入。我们对数据进行了初步分析,展示了ML需要克服的一些挑战,和ML可以利用的一些特征,以及一些潜在的研究方向的提示。