Research Article
A Primer on Large Intelligent Surface (LIS) for Wireless Sensing in an Industrial Setting
@INPROCEEDINGS{10.1007/978-3-030-73423-7_10, author={Cristian J. Vaca-Rubio and Pablo Ramirez-Espinosa and Robin Jess Williams and Kimmo Kansanen and Zheng-Hua Tan and Elisabeth de Carvalho and Petar Popovski}, title={A Primer on Large Intelligent Surface (LIS) for Wireless Sensing in an Industrial Setting}, proceedings={Cognitive Radio-Oriented Wireless Networks. 15th EAI International Conference, CrownCom 2020, Rome, Italy, November 25-26, 2020, Proceedings}, proceedings_a={CROWNCOM}, year={2021}, month={3}, keywords={}, doi={10.1007/978-3-030-73423-7_10} }- Cristian J. Vaca-Rubio
Pablo Ramirez-Espinosa
Robin Jess Williams
Kimmo Kansanen
Zheng-Hua Tan
Elisabeth de Carvalho
Petar Popovski
Year: 2021
A Primer on Large Intelligent Surface (LIS) for Wireless Sensing in an Industrial Setting
CROWNCOM
Springer
DOI: 10.1007/978-3-030-73423-7_10
Abstract
One of the beyond-5G developments that is often highlighted is the integration of wireless communication and radio sensing. This paper addresses the potential of communication-sensing integration of Large Intelligent Surfaces (LIS) in an exemplary Industry 4.0 scenario. Besides the potential for high throughput and efficient multiplexing of wireless links, an LIS can offer a high-resolution rendering of the propagation environment. This is because, in an indoor setting, it can be placed in proximity to the sensed phenomena, while the high resolution is offered by densely spaced tiny antennas deployed over a large area. By treating an LIS as a radio image of the environment, we develop sensing techniques that leverage the usage of computer vision combined with machine learning. We test these methods for a scenario where we need to detect whether an industrial robot deviates from a predefined route. The results show that the LIS-based sensing offers high precision and has a high application potential in indoor industrial environments.
