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：无线通信向6G及更高版本的演进预计将依赖于新型机器学习能力。这些能力可使无线网络组件做出前瞻性决策并采取行动，从而保障服务质量与用户体验。此外，车联网通信与工业通信领域将涌现新的应用场景。特别是在车联网领域，车联网方案将从这些进展中受益显著。基于此，我们开展了一项详尽的测量活动，为开展多样化机器学习研究奠定了基础。生成的数据集涵盖了城市环境中蜂窝网络（含两家不同运营商）与侧链路无线接入技术的GPS定位无线测量数据，能够支持面向车联网的多方向研究。数据集以高时间分辨率进行标注与采样，并公开提供完整配套信息，便于新研究者快速上手。我们通过初步数据分析，揭示了机器学习需应对的挑战与可有效利用的特征，同时指出潜在的研究方向。