V2V- and V2X-Communication Data within a Distributed Computing Platform for Adaptive Radio channel Modelling
This paper presents findings of the collection and exploitation process of V2X communication data with the aim of developing a measurement data-based radio channel model for the ITS frequency range around 5.9 GHz. Collected under real world conditions, connectivity quality measurements of ETSI ITS G5 communication data form the basis of the presented model prototype. The paper provides insight into the installation and configuration of the communication hardware used. Furthermore, the transmission process of accumulated as well as live data from the vehicles to a big data platform using the IoT message protocol MQTT is investigated. There, the communication data is enriched with other geographically referenced open source data. Finally, the development of a prototype V2X radio channel model using a machine learning process is presented. The model is a helpful instrument for predicting reception qualities in the ITS radio range for previously unknown receiver positions and thus a prerequisite for two exemplary presented use cases.