Assessing Wireless Sensing Potential with Large Intelligent Surfaces

TL;DR

This paper explores the use of Large Intelligent Surfaces (LIS) for high-resolution environmental sensing in industrial settings, leveraging image processing, machine learning, and statistical tests to detect deviations in robot paths.

Contribution

It introduces a novel LIS-based sensing approach using image processing and machine learning, with a super-resolution technique and statistical benchmark for industrial environment monitoring.

Findings

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

Machine learning enhances super-resolution imaging for environment sensing.

The proposed methods show high potential for indoor industrial applications.

Abstract

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 holographic 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.