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

本文提出了一种方法将车辆交通模拟器和光线追踪模拟器结合起来，生成代表 5G 情境的信道实现，并描述了一个特定的数据集，用于研究基于毫米波的车辆对基础设施的波束选举技术，并利用这个方法生成的数据集进行分类，回归和强化学习问题的实验。