TY - JOUR
T1 - Assessing Wireless Sensing Potential with Large Intelligent Surfaces
AU - Vaca Rubio, Cristian Jesús
AU - Espinosa, Pablo Ramirez
AU - Kansanen, Kimmo
AU - Tan, Zheng-Hua
AU - De Carvalho, Elisabeth
AU - Popovski, Petar
PY - 2021/4
Y1 - 2021/4
N2 - Sensing capability is one of the most highlighted new feature of future 6G wireless networks. This paper addresses the sensing potential of Large Intelligent Surfaces (LIS) in an exemplary Industry 4.0 scenario. Besides the attention received by LIS in terms of communication aspects, it 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 relying on the received signal power, we develop techniques to sense the environment, by leveraging the tools of image processing and machine learning. Once a radio image is obtained, a Denoising Autoencoder (DAE) network can be used for constructing a super-resolution image leading to sensing advantages not available in traditional sensing systems. Also, we derive a statistical test based on the Generalized Likelihood Ratio (GLRT) as a benchmark for the machine learning solution. 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 - Sensing capability is one of the most highlighted new feature of future 6G wireless networks. This paper addresses the sensing potential of Large Intelligent Surfaces (LIS) in an exemplary Industry 4.0 scenario. Besides the attention received by LIS in terms of communication aspects, it 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 relying on the received signal power, we develop techniques to sense the environment, by leveraging the tools of image processing and machine learning. Once a radio image is obtained, a Denoising Autoencoder (DAE) network can be used for constructing a super-resolution image leading to sensing advantages not available in traditional sensing systems. Also, we derive a statistical test based on the Generalized Likelihood Ratio (GLRT) as a benchmark for the machine learning solution. 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.
KW - Computer vision
KW - industry 4.0
KW - large intelligent surfaces
KW - machine learning
KW - sensing
UR - http://www.scopus.com/inward/record.url?scp=85119577064&partnerID=8YFLogxK
U2 - 10.1109/OJCOMS.2021.3073467
DO - 10.1109/OJCOMS.2021.3073467
M3 - Journal article
VL - 2
SP - 934
EP - 947
JO - IEEE Open Journal of the Communications Society
JF - IEEE Open Journal of the Communications Society
SN - 2644-125X
M1 - 9405304
ER -