Radio Sensing with Large Intelligent Surface for 6G
Cristian J. Vaca-Rubio, Pablo Ramirez-Espinosa, Kimmo Kansanen, Zheng-Hua Tan, Elisabeth de Carvalho
Abstract
This paper leverages the potential of Large Intelligent Surfaces (LIS) for radio sensing in 6G wireless networks. By taking advantage of arbitrary communication signals occurring in the scenario, we apply direct processing to the output signal from the LIS to obtain a radio map that describes the physical presence of passive devices (scatterers, humans) which act as virtual sources due to the communication signal reflections. We then assess the usage of machine learning and computer vision methods including clustering, template matching and component labeling to extract meaningful information from these radio maps. As an exemplary use case, we evaluate this method for passive multi-human detection in an indoor setting. The results show that the presented method has high application potential as we are able to detect around 98% of humans passively even in quite unfavorable Signal-to-Noise Ratio (SNR) conditions.
BibTeX
@inproceedings{icassp2023_radiosensingwith,
title = {Radio Sensing with Large Intelligent Surface for 6G},
author = {Cristian J. Vaca-Rubio and Pablo Ramirez-Espinosa and Kimmo Kansanen and Zheng-Hua Tan and Elisabeth de Carvalho},
booktitle = {ICASSP 2023},
year = {2023}
}