Assessing Wireless Sensing Potential With Large Intelligent Surfaces
| Autor: | Cristian J. Vaca-Rubio, Pablo Ramirez-Espinosa, Kimmo Kansanen, Zheng-Hua Tan, Elisabeth De Carvalho, Petar Popovski |
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| Rok vydání: | 2021 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Computer Science - Machine Learning Computer science Computer Vision and Pattern Recognition (cs.CV) Real-time computing Computer Science - Computer Vision and Pattern Recognition Image processing TK5101-6720 02 engineering and technology Machine Learning (cs.LG) Rendering (computer graphics) law.invention Industrial robot 0203 mechanical engineering law large intelligent surfaces FOS: Electrical engineering electronic engineering information engineering 0202 electrical engineering electronic engineering information engineering Wireless Electrical Engineering and Systems Science - Signal Processing industry 4.0 sensing HE1-9990 Wireless network business.industry 020302 automobile design & engineering 020206 networking & telecommunications machine learning Feature (computer vision) Telecommunication Benchmark (computing) Computer vision business Transportation and communications Wireless sensor network |
| Zdroj: | IEEE Open Journal of the Communications Society IEEE Open Journal of the Communications Society, Vol 2, Pp 934-947 (2021) Vaca Rubio, C J, Espinosa, P R, Kansanen, K, Tan, Z-H, De Carvalho, E & Popovski, P 2021, ' Assessing Wireless Sensing Potential with Large Intelligent Surfaces ', IEEE Open Journal of the Communications Society, vol. 2, 9405304, pp. 934-947 . https://doi.org/10.1109/OJCOMS.2021.3073467 |
| ISSN: | 2644-125X |
| Popis: | 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. Comment: arXiv admin note: text overlap with arXiv:2006.06563 |
| Databáze: | OpenAIRE |
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