TY - GEN
T1 - Characterization of mmWave Channel Properties at 28 and 60 GHz in Factory Automation Deployments
AU - Solomitckii, Dmitrii
AU - Orsino, Antonino
AU - Andreev, Sergey
AU - Koucheryavy, Yevgeni
AU - Valkama, Mikko
N1 - jufoid=57592
INT=elt,"Orsino, Antonino"
PY - 2018/6/8
Y1 - 2018/6/8
N2 - Future cellular systems are expected to revolutionize today's industrial ecosystem by satisfying the stringent requirements of ultra-high reliability and extremely low latency. Along these lines, the core technology to support the next-generation factory automation deployments is the use of millimeter-wave (mmWave) communication that operates at extremely high frequencies (i.e., from 10 to 100 GHz). However, characterizing the radio propagation behavior in realistic factory environments is challenging due to shorter mmWave wavelengths, which make channel properties be sensitive to the actual topology and size of the surrounding objects. For these reasons, this paper studies the important mmWave channel properties for two distinct types of factories, namely, light industry and heavy industry. These represent the extreme cases of factory classification based on the level of technology, the density and the size of the equipment, and the goods produced. Accordingly, we assess the candidate mmWave frequencies of 28 and 60 GHz for licensed-and unlicensed-band communication, respectively. After analyzing the signal propagation (e.g., in terms of path loss) and the line-of-sight (LoS) probability, our understanding is that in a factory automation environment the presence of metallic equipment and various objects produces many dissimilarities in the mmWave channel properties, thus making them difficult to describe with conventional empirical or stochastic models. Our findings suggest that the deployment of the practical mmWave systems in indoor industrial environments should not therefore rely on past propagation studies available in the literature blindly but might take into account more accurate and reliable evaluation of the environment that is possible with ray-based simulations.
AB - Future cellular systems are expected to revolutionize today's industrial ecosystem by satisfying the stringent requirements of ultra-high reliability and extremely low latency. Along these lines, the core technology to support the next-generation factory automation deployments is the use of millimeter-wave (mmWave) communication that operates at extremely high frequencies (i.e., from 10 to 100 GHz). However, characterizing the radio propagation behavior in realistic factory environments is challenging due to shorter mmWave wavelengths, which make channel properties be sensitive to the actual topology and size of the surrounding objects. For these reasons, this paper studies the important mmWave channel properties for two distinct types of factories, namely, light industry and heavy industry. These represent the extreme cases of factory classification based on the level of technology, the density and the size of the equipment, and the goods produced. Accordingly, we assess the candidate mmWave frequencies of 28 and 60 GHz for licensed-and unlicensed-band communication, respectively. After analyzing the signal propagation (e.g., in terms of path loss) and the line-of-sight (LoS) probability, our understanding is that in a factory automation environment the presence of metallic equipment and various objects produces many dissimilarities in the mmWave channel properties, thus making them difficult to describe with conventional empirical or stochastic models. Our findings suggest that the deployment of the practical mmWave systems in indoor industrial environments should not therefore rely on past propagation studies available in the literature blindly but might take into account more accurate and reliable evaluation of the environment that is possible with ray-based simulations.
KW - 5G systems
KW - Factory automation
KW - Industrial IoT
KW - MmWave communication
KW - Radio channel properties
KW - Ray-based modeling
U2 - 10.1109/WCNC.2018.8377337
DO - 10.1109/WCNC.2018.8377337
M3 - Conference contribution
AN - SCOPUS:85049223529
SP - 1
EP - 6
BT - 2018 IEEE Wireless Communications and Networking Conference, WCNC 2018
PB - IEEE
T2 - IEEE Wireless Communications and Networking Conference
Y2 - 1 January 1900
ER -