TY - GEN
T1 - ViWi
T2 - 91st IEEE Vehicular Technology Conference, VTC Spring 2020
AU - Alrabeiah, Muhammad
AU - Hredzak, Andrew
AU - Liu, Zhenhao
AU - Alkhateeb, Ahmed
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/5
Y1 - 2020/5
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85088290043&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85088290043&partnerID=8YFLogxK
U2 - 10.1109/VTC2020-Spring48590.2020.9128579
DO - 10.1109/VTC2020-Spring48590.2020.9128579
M3 - Conference contribution
AN - SCOPUS:85088290043
T3 - IEEE Vehicular Technology Conference
BT - 2020 IEEE 91st Vehicular Technology Conference, VTC Spring 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 25 May 2020 through 28 May 2020
ER -