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Rain analysis on KA-band for terrestrial link point to point communication in Malaysia

Radio waves propagate through the earth's atmosphere will be attenuated due to presence of the atmosphere particles such as water vapor, water drops and the ice particles. Meantime the atmospheric gases and rain will absorb the scatter the radio path. Consequently, degrades the performance of t...

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Bibliographic Details
Main Authors: Ulaganathen, Kesavan, Rahman, Tharek A., Islam, Rafiqul M., Abdullah, Khaizuran
Format: Conference Proceeding
Language:English
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Summary:Radio waves propagate through the earth's atmosphere will be attenuated due to presence of the atmosphere particles such as water vapor, water drops and the ice particles. Meantime the atmospheric gases and rain will absorb the scatter the radio path. Consequently, degrades the performance of the link. This paper presents studies on rain attenuation at 26 GHz, which is widely used for local multipoint distribution service deployment by using the measured and prediction methods for terrestrial microwave links point to point in tropical regions. Several models have been proposed by researchers to account for the horizontal variation of rain fall. Since the ITU-R models are still the most widely used for rain attenuation predictions in global. The ITU-R model for the terrestrial link, which is known as ITU-R P530-13 have been revised to P530-14 in February 2012. The objective of this study is to compare the rain attenuation prediction variation for the both previous and updated ITU-R model with actual measurement data at operating frequency of 26 GHz. This information will provide useful information for researchers by making good judgments, understand and makes good considerations in rain attenuation predictions for a terrestrial link especially operating frequency at 26 GHz in a tropical region. It was found that the previous ITU-R rain attenuation prediction P530-13 closely agrees with actual measurement to compare to the latest ITU-R P530-14 version. However, both of these models underestimate at all percentage of time.
DOI:10.1109/MICC.2013.6805818