TITLE: Berlin V2X: A Machine Learning Dataset from Multiple Vehicles and Radio Access Technologies 

ABSTRACT: 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.
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.
Berlin V2X: A Machine Learning Dataset from Multiple Vehicles and Radio Access Technologies
http://arxiv.org/abs/2212.10343v3