Rain-induced attenuation above 10 GHz can be severe at heavy rain rates resulting in deep fading, which can negatively impact the quality of receive signal level at the Earth station receiver. The dearth of direct measurement data in most of the tropical and equatorial climates has motivated the campaign for collection of rain attenuation data on slant paths in these regions. This is mainly due to a huge receiver margin required for such measurement, and which is very difficult to obtain by using a spectrum analyzer. The measurement results of rain rates and rain-induced attenuation in vertically polarized signals propagating at 10.982 GHz in a tropical Malaysian climate are presented in this study. The measured attenuation is compared with large-scale prediction models. As shown in the statistically-tested results, the Bryant model yields the best overall fit, while the Crane model yields the worst overall fit. The results show that the models have relatively good prediction capabilities in the Malaysian tropical climate; however, their prediction errors still need to be minimized. Therefore, in this study, a correction factor is proposed to enhance their predictions.
Rain-induced attenuation above 10 GHz can be severe at heavy rain rates resulting in deep fading, which can negatively impact the quality of receive signal level at the Earth station receiver. The dearth of direct measurement data in most of the tropical and equatorial climates has motivated the campaign for collection of rain attenuation data on slant paths in these regions. This is mainly due to a huge receiver margin required for such measurement, and which is very difficult to obtain by using a spectrum analyzer. The measurement results of rain rates and rain-induced attenuation in vertically polarized signals propagating at 10.982 GHz in a tropical Malaysian climate are presented in this study. The measured attenuation is compared with large-scale prediction models. As shown in the statistically-tested results, the Bryant model yields the best overall fit, while the Crane model yields the worst overall fit. The results show that the models have relatively good prediction capabilities in the Malaysian tropical climate; however, their prediction errors still need to be minimized. Therefore, in this study, a correction factor is proposed to enhance their predictions.
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