The growing role that artificial intelligence and specifically machine
learning is playing in shaping the future of wireless communications has opened
up many new and intriguing research directions. This paper motivates the
research in the novel direction of \textit{vision-aided wireless
communications}, which aims at leveraging visual sensory information in
tackling wireless communication problems. Like any new research direction
driven by machine learning, obtaining a development dataset poses the first and
most important challenge to vision-aided wireless communications. This paper
addresses this issue by introducing the Vision-Wireless (ViWi) dataset
framework. It is developed to be a parametric, systematic, and scalable data
generation framework. It utilizes advanced 3D-modeling and ray-tracing
softwares to generate high-fidelity synthetic wireless and vision data samples
for the same scenes. The result is a framework that does not only offer a way
to generate training and testing datasets but helps provide a common ground on
which the quality of different machine learning-powered solutions could be
assessed.

本文介绍了一种利用 3D 建模和射线跟踪软件生成高保真合成无线电和视觉数据样本的 Vision-Wireless 数据集框架。该框架旨在为新的机器学习算法提供一种生成训练和测试数据集的方式，并帮助评估不同算法的质量。