33,094 research outputs found
Enabling Communication Technologies for Automated Unmanned Vehicles in Industry 4.0
Within the context of Industry 4.0, mobile robot systems such as automated
guided vehicles (AGVs) and unmanned aerial vehicles (UAVs) are one of the major
areas challenging current communication and localization technologies. Due to
stringent requirements on latency and reliability, several of the existing
solutions are not capable of meeting the performance required by industrial
automation applications. Additionally, the disparity in types and applications
of unmanned vehicle (UV) calls for more flexible communication technologies in
order to address their specific requirements. In this paper, we propose several
use cases for UVs within the context of Industry 4.0 and consider their
respective requirements. We also identify wireless technologies that support
the deployment of UVs as envisioned in Industry 4.0 scenarios.Comment: 7 pages, 1 figure, 1 tabl
Impact of polarization diversity in massive MIMO for industry 4.0
The massive polarimetric radio channel is evaluated in an indoor industrial scenario at 3.5 GHz using a 10×10 uniform rectangular array (URA). The analysis is based on (1) propagation characteristics like the average received gain and the power to interference ratio from the Gram matrix and (2) system-oriented metrics such as sum-rate capacity with maximum-ratio transmitter (MRT). The results clearly show the impact of polarization diversity in an industrial scenario and how it can considerably improve different aspects of the system design. Results for sum-rate capacity are promising and show that the extra degree of freedom, provided by polarization diversity, can optimize the performance of a very simple precoder, the MRT
Caching-Aided Collaborative D2D Operation for Predictive Data Dissemination in Industrial IoT
Industrial automation deployments constitute challenging environments where
moving IoT machines may produce high-definition video and other heavy sensor
data during surveying and inspection operations. Transporting massive contents
to the edge network infrastructure and then eventually to the remote human
operator requires reliable and high-rate radio links supported by intelligent
data caching and delivery mechanisms. In this work, we address the challenges
of contents dissemination in characteristic factory automation scenarios by
proposing to engage moving industrial machines as device-to-device (D2D)
caching helpers. With the goal to improve reliability of high-rate
millimeter-wave (mmWave) data connections, we introduce the alternative
contents dissemination modes and then construct a novel mobility-aware
methodology that helps develop predictive mode selection strategies based on
the anticipated radio link conditions. We also conduct a thorough system-level
evaluation of representative data dissemination strategies to confirm the
benefits of predictive solutions that employ D2D-enabled collaborative caching
at the wireless edge to lower contents delivery latency and improve data
acquisition reliability
A Software-Defined Channel Sounder for Industrial Environments with Fast Time Variance
Novel industrial wireless applications require wideband, real-time channel
characterization due to complex multipath propagation. Rapid machine motion
leads to fast time variance of the channel's reflective behavior, which must be
captured for radio channel characterization. Additionally, inhomogeneous radio
channels demand highly flexible measurements. Existing approaches for radio
channel measurements either lack flexibility or wide-band, real-time
performance with fast time variance. In this paper, we propose a correlative
channel sounding approach utilizing a software-defined architecture. The
approach enables real-time, wide-band measurements with fast time variance
immune to active interference. The desired performance is validated with a
demanding industrial application example.Comment: Submitted to the 15th International Symposium on Wireless
Communication Systems (ISWCS 2018
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