Signal Attenuation Prediction Model for a 22 GHz Terrestrial Communication Link in Sudan Due to Dust and Sand Storms Using Machine Learning

Signal Attenuation Prediction Model for a 22 GHz Terrestrial Communication Link in Sudan Due to Dust and Sand Storms Using Machine Learning

Signal attenuation due to dust and sand storms is one of the major problems in the utilization of microwave frequency bands for terrestrial and space communication especially in arid regions such as Northern Africa and Middle Eastern regions. In this paper, a machine learning (ML) model is developed...

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Bibliographic Details
Published in:IEEE access Vol. 9; pp. 164632 - 164642
Main Authors: Shamim, Mohammed Zubair M., Elsheikh, Elfatih Abdelrahman Ahmed, Salih, Fakher Eldin Mohamed Suliman, Islam, Md Rafiqul
Format: Journal Article
Language:English
Published: Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Arid regions
Atmospheric attenuation
Atmospheric measurements
Atmospheric modeling
Attenuation
Dust and sand storms
Dust storms
Humidity
Machine learning
Meteorological data
Microwave attenuation
Microwave communication
Microwave frequencies
Microwave measurement
microwave signal attenuation
Prediction models
Relative humidity
Sand
Space communication
Storms
terrestrial communication
Visibility
Wind speed
Sudan
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Summary:Signal attenuation due to dust and sand storms is one of the major problems in the utilization of microwave frequency bands for terrestrial and space communication especially in arid regions such as Northern Africa and Middle Eastern regions. In this paper, a machine learning (ML) model is developed to predict microwave signal attenuation due to atmospheric conditions recorded during dust and sand storms. The model utilizes recorded meteorological data, particularly optical visibility, temperature, relative humidity, atmospheric pressure, and wind speed conditions to predict the signal attenuation for a 22GHz operating frequency terrestrial link in Sudan. Compared to measured values, the predicted signal attenuation values show a relatively optimistic relation between meteorological data and microwave signal attenuation when utilizing all the meteorological features.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3132700