Rain Attenuation Prediction Model for Terrestrial Links Using Gaussian Process Regression

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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.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2021-11
Language
English
Article Type
Article
Citation

IEEE COMMUNICATIONS LETTERS, v.25, no.11, pp.3719 - 3723

ISSN
1089-7798
DOI
10.1109/LCOMM.2021.3109619
URI
http://hdl.handle.net/10203/289355
Appears in Collection
MA-Journal Papers(저널논문)
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