A Primer on Large Intelligent Surface (LIS) for Wireless Sensing in an Industrial Setting

TL;DR

This paper explores the use of Large Intelligent Surfaces (LIS) for high-resolution wireless sensing in industrial environments, combining radio imaging with machine learning to detect robot deviations with high precision.

Contribution

It introduces novel sensing techniques using LIS as radio images, integrating computer vision and machine learning for industrial robot monitoring.

Findings

LIS-based sensing achieves high precision in detecting robot deviations.

The approach demonstrates high potential for indoor industrial applications.

LIS provides detailed environmental rendering for improved sensing accuracy.

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.