Assessing Wireless Sensing Potential with Large Intelligent Surfaces

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:2006.0656

Throughput-based quality adaptation for DASH in 5G mobile networks

Video streaming in mobile networks is currently the most widely used service and its usage is expected to grow exponentially in the next years. Due to the changing conditions of the radio interface, techniques likes Dynamic Adaptive Streaming over HTTP (DASH) allows the user equipment to request the video coding rate that better matches the instantaneous network capacity. There are three types of algorithms to select the appropriate video coding rate based on different types of quality of service metrics: throughput-based, buffer-based and hybrid. In this paper we present three different versions of a throughput-based algorithm, comparing their performance in terms of mean and mode of the video quality index as well as the number of overlapping video chunks. We focus on the end-user quality of experience to evaluate which is the implementation that optimizes the performance.Universidad de Málaga, Plan Nacional I+D (Ministerio de Economía y Competitividad) bajo el proyecto TEC2016-80090-C2-1-R. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

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

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