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Comparative Analysis of Channel Models for Industrial IoT Wireless Communication

Authors :
Wenbo Wang
Stefan L. Capitaneanu
Dana Marinca
Elena-Simona Lohan
Tampere University
Electrical Engineering
Doctoral Programme in Computing and Electrical Engineering
Research group: Wireless Communications and Positioning
Source :
IEEE Access, Vol 7, Pp 91627-91640 (2019)
Publication Year :
2019

Abstract

In the industrial environments of the future, robots, sensors, and other industrial devices will have to communicate autonomously and in a robust and efficient manner with each other, relying on a large extent on wireless communication links, which will expand and supplement the existing wired/Ethernet connections. The wireless communication links suffer from various channel impairments, such as attenuations due to path losses, random fluctuations due to shadowing and fading effects over the channel and the non line-of-sight (NLoS) due to obstacles on the communication path. Several channel models exist to model the industrial environments in indoor, urban, or rural areas, but a comprehensive comparison of their characteristics is still missing from the current literature. Moreover, several IoT technologies are already on the market, many competing with each other for future possible services and applications in Industrial IoT (IIoT) environments. This paper aims at giving a survey of existing wireless channel models applicable to the IIoT context and to compare them for the first time in terms of worst-case, median-case, and best-case predictive behaviors. Performance metrics, such as cell radius, spectral efficiency, and outage probability, are investigated with a focus on three long-range IoT technologies, one medium-range, and one short-range IoT technology as selected case studies. A summary of popular IoT technologies and their applicability to industrial scenarios is addressed as well. publishedVersion

Details

Language :
English
Database :
OpenAIRE
Journal :
IEEE Access, Vol 7, Pp 91627-91640 (2019)
Accession number :
edsair.dedup.wf.001..04b71f2a2b7007f05cab0200f3f2ef99