This dataset provides wireless measurements from two industrial testbeds: iV2V (industrial Vehicle-to-Vehicle) and iV2I+ (industrial Vehicular-to-Infrastructure plus sensor). iV2V covers sidelink communication scenarios between Automated Guided Vehicles (AGVs), while iV2I+ was conducted at an industrial setting where an autonomous cleaning robot is connected to a private cellular network. The datasets are labelled and pre-filtered for a fast on-boarding and applicability. The common measurement methodology to both datasets pursues an application to Machine Learning (ML) for tasks such as fingerprinting, line-of-sight detection, prediction of quality of service or link selection, among others. 

The Berlin V2X dataset offers high-resolution GPS-located wireless measurements across diverse urban environments in the city of Berlin for both cellular and sidelink radio access technologies, acquired with up to 4 cars over 3 days. The data enables thus a variety of different ML studies towards vehicle-to-anything (V2X) communication.The data includes information onphysical layer parameters (such as signal strength and signal quality)cellular radio resource management like cell identity, carrier aggregation and assigned resource blockswireless Quality of Service (QoS) like delay and throughput (for cellular) or packet error rate (for sidelink)positioning information.The datasets are labelled and pre-filtered for a fast on-boarding and applicability. The measurement methodology pursues an application to Machine Learning (ML) for tasks such as QoS prediction, transfer learning, proactive radio resource allocation or link selection, among others.