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.

中文翻译：

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评估具有大面积智能表面的无线传感潜力

传感能力是未来6G无线网络最突出的新功能之一。本文探讨了示例性工业4.0场景中大型智能表面（LIS）的感测潜力。LIS除了在通信方面受到关注外，还可以提供传播环境的高分辨率渲染。这是因为，在室内环境中，可以将其放置在感测到的现象附近，而高分辨率则是通过在大面积上部署的密集小天线来提供的。通过将LIS视为依赖于接收信号功率的环境的无线电图像，我们开发了利用图像处理和机器学习工具来感测环境的技术。一旦获得无线电图像，去噪自动编码器（DAE）网络可用于构建超分辨率图像，从而带来传统传感系统无法获得的传感优势。此外，我们基于广义似然比（GLRT）作为机器学习解决方案的基准派生了统计测试。我们针对需要检测工业机器人是否偏离预定路线的场景测试了这些方法。结果表明，基于LIS的传感具有很高的精度，在室内工业环境中具有很高的应用潜力。我们针对需要检测工业机器人是否偏离预定路线的场景测试了这些方法。结果表明，基于LIS的传感具有很高的精度，在室内工业环境中具有很高的应用潜力。我们针对需要检测工业机器人是否偏离预定路线的场景测试了这些方法。结果表明，基于LIS的传感具有很高的精度，在室内工业环境中具有很高的应用潜力。