ViWi: A Deep Learning Dataset Framework for Vision-Aided Wireless Communications

TL;DR

This paper introduces ViWi, a comprehensive dataset framework that combines high-fidelity synthetic visual and wireless data for advancing vision-aided wireless communication research.

Contribution

It presents a scalable, parametric data generation framework using 3D modeling and ray-tracing to support machine learning development in vision-aided wireless communications.

Findings

Provides high-quality synthetic datasets for research

Enables systematic evaluation of machine learning solutions

Facilitates development of vision-aided wireless communication models

Abstract

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.