  The increasing complexity of configuring cellular networks suggests that
machine learning (ML) can effectively improve 5G technologies. Deep learning
has proven successful in ML tasks such as speech processing and computational
vision, with a performance that scales with the amount of available data. The
lack of large datasets inhibits the flourish of deep learning applications in
wireless communications. This paper presents a methodology that combines a
vehicle traffic simulator with a raytracing simulator, to generate channel
realizations representing 5G scenarios with mobility of both transceivers and
objects. The paper then describes a specific dataset for investigating
beams-election techniques on vehicle-to-infrastructure using millimeter waves.
Experiments using deep learning in classification, regression and reinforcement
learning problems illustrate the use of datasets generated with the proposed
methodology
