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

Muhammad Alrabeiah, Andrew Hredzak, Zhenhao Liu, Ahmed Alkhateeb

Research output: Chapter in Book/Report/Conference proceedingConference contribution

33 Scopus citations


The growing role 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 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.

Original languageEnglish (US)
Title of host publication2020 IEEE 91st Vehicular Technology Conference, VTC Spring 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728152073
StatePublished - May 2020
Event91st IEEE Vehicular Technology Conference, VTC Spring 2020 - Antwerp, Belgium
Duration: May 25 2020May 28 2020

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252


Conference91st IEEE Vehicular Technology Conference, VTC Spring 2020

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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