TY - GEN
T1 - A Primer on Large Intelligent Surface (LIS) for Wireless Sensing in an Industrial Setting
AU - Vaca Rubio, Cristian Jesús
AU - Espinosa, Pablo Ramirez
AU - Williams, Robin Jess
AU - Kansanen, Kimmo
AU - Tan, Zheng-Hua
AU - De Carvalho, Elisabeth
AU - Popovski, Petar
PY - 2021/3/31
Y1 - 2021/3/31
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85107378431&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-73423-7_10
DO - 10.1007/978-3-030-73423-7_10
M3 - Article in proceeding
SN - 978-3-030-73422-0
T3 - Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
SP - 126
EP - 138
BT - EAI CROWNCOM 2020 - 15th EAI International Conference on Cognitive Radio Oriented Wireless Networks
A2 - Caso, Giuseppe
A2 - De Nardis, Luca
A2 - Gavrilovska, Liljana
PB - Springer
T2 - 15th EAI International Conference on Cognitive Radio Oriented Wireless Networks
Y2 - 25 November 2020 through 26 November 2020
ER -