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 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.
Highlights
M ASSIVE multiple-input multiple-output (MIMO) is one of the essential technologies in the 5th generation of wireless networks (5G) [2]
Looking towards the 6th generation of wireless networks (6G), there are some significant breakthroughs on the design of reprogramable metamaterials [4], giving raise to new concepts such as holographic MIMO surfaces (HMIMO) [5], large intelligent surface (LIS) [6] and reconfigurable intelligent surface (RIS) [7]
Due to our specific use case, and the training data constraints, we propose the use of an unsupervised machine learning (ML) algorithm named as local outlier factor (LOF) which identifies the outliers presents in a dataset [47]
Summary
M ASSIVE multiple-input multiple-output (MIMO) is one of the essential technologies in the 5th generation of wireless networks (5G) [2]. Despite all the available works dealing with beyond massive MIMO and sensing, both topics have been addressed rather separately This has motivated the present work, where the objective is to assess the potential of the combined use of DL algorithms and large surfaces for the purpose of sensing the propagation environment. These power samples are processed to generate a high resolution image of the propagation environment that can be used to feed ML algorithms to sense largescale events. Note the LIS elements are using the CSI information as envelope detectors, as no phase estimation is needed but the received signal power Using this approach, the complexity of the multipath propagation is reduced to using information represented as an image. VACA-RUBIO et al.: ASSESSING WIRELESS SENSING POTENTIAL WITH LIS that LIS image based wireless sensing is proposed in the literature
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