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Journal Article Rain Attenuation Prediction Model for Terrestrial Links Using Gaussian Process Regression
Cited 9 time in scopus Share share facebook twitter linkedin kakaostory
Authors
Ki Joung Jang, Youngkeun Yoon, Junseok Kim, Jong Ho Kim, Ganguk Hwang
Issue Date
2021-11
Citation
IEEE Communications Letters, v.25, no.11, pp.3719-3723
ISSN
1089-7798
Publisher
IEEE
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.1109/LCOMM.2021.3109619
Abstract
Rainfall is considered as one of the most crucial atmospheric elements that cause attenuation in signal propagation, especially in high-frequency range for fifth-generation (5G) and beyond wireless networks. To unveil the complex relationships between rain attenuation and other factors including rainfall rate, we propose a new rain attenuation prediction model for terrestrial line-of-sight (LoS) propagation using Gaussian Process Regression (GPR). In the proposed model the Recommendation ITU-R P.530 (called the ITU-R model) is used as the mean function in GPR, and to capture the deviation of measured rain attenuation from the ITU-R model we develop a latent function in GPR motivated from the ITU-R model. We validate with the ITU-R study group 3 databank (DBSG3) that the proposed model provides high prediction accuracy.
KSP Keywords
Atmospheric elements, Fifth Generation(5G), Frequency Range, Gaussian process regression, High Frequency(HF), ITU-R model, International telecommunications union radiocommunication(ITU-R), Latent function, Line-Of-Sight(LOS), Prediction accuracy, Proposed model