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 on * physical layer parameters (such as signal strength and signal quality) * cellular radio resource management like cell identity, carrier aggregation and assigned resource blocks * wireless 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.
This dataset provides wireless measurements from two industrial testbeds: iV2V (industrial Vehicle-to-Vehicle) and iV2I+ (industrial Vehicular-to-Infrastructure plus sensor). iV2V covers 10h of sidelink communication scenarios between 3 Automated Guided Vehicles (AGVs), while iV2I+ was conducted for around 16h at an industrial site where an autonomous cleaning robot is connected to a private cellular network. The data includes information on physical layer parameters (such as signal strength and signal quality), wireless Quality of Service (QoS) like delay and throughput, and positioning information. 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, QoS prediction or link selection, among others.