  This paper leverages the potential of Large Intelligent Surface (LIS) for
radio sensing in 6G wireless networks. Major research has been undergone about
its communication capabilities but it can be exploited as a formidable tool for
radio sensing. 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 (k-means
clustering), image processing and computer vision (template matching and
component labeling) to extract meaningful information from these radio maps. As
an exemplary use case, we evaluate this method for both active and passive user
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 and 100% active users by just using communication signals of
commodity devices even in quite unfavorable Signal-to-Noise Ratio (SNR)
conditions.
