5 research outputs found
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 EconomÃa y Competitividad) bajo el proyecto TEC2016-80090-C2-1-R.
Universidad de Málaga. Campus de Excelencia Internacional AndalucÃa 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