Analysis of Rain-Induced Attenuation at Ku-V Band Using Gamma Drop Size Distribution Model Over a Tropical Region

Ojo, J. S.; Akoma, D.B.; Adetan, Oluwumi; Falodun, S E

doi:10.1007/s11277-021-09317-y

Cited by 2 publications

(2 citation statements)

References 29 publications

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“…These large raindrops can cause significant scattering and absorption of microwave signals, leading to a decrease in signal strength and an increase in error rates. It has been observed that the size distribution of raindrops can vary depending on the geographical location, which in turn strongly influences the specific attenuation caused by rainfall and, consequently, the overall rain attenuation [8,9]. Rainfall intensity, on the other hand, can also vary significantly depending on the location and meteorological conditions, further impacting the extent of signal degradation caused by rainfall [7,10].…”

Section: Introductionmentioning

confidence: 99%

Rainfall’s Symphony: Understanding Its Influence on Communication Systems in Nigeria

Obiyemi,

Moloi

2024

Rainfall - Observations and Modelling

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This study investigates the significant issue of rain-induced signal attenuation in satellite communication, specifically focusing on television receive-only (TVRO) stations operating in the Ku band across diverse Nigerian locations. Utilizing a comprehensive dataset of point rain rate distribution and the ITU rain attenuation model, the study comprehensively assesses how rainfall impacts signal quality. The findings highlight that southern regions consistently display high decibel (dB) values, indicating increased susceptibility to signal disruptions during heavy rainfall, while a comparative analysis between two key satellites, EUTELSAT 36B and INTELSAT 20, consistently favors the former in terms of signal resilience during adverse weather conditions. In contrast, northern regions generally exhibit lower dB values, suggesting a higher degree of signal resilience during rainfall events. These insights underscore the importance of considering location-specific and satellite-specific factors when designing satellite communication systems, emphasizing the need for robust infrastructure and strategic satellite selection to mitigate the effects of rain-induced attenuation. This study provides valuable guidance to engineers and service providers, enabling them to make informed decisions to minimize signal disruptions and enhance overall network reliability, particularly in regions susceptible to rain-induced attenuation.

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Section: Introductionmentioning

confidence: 99%

Rainfall’s Symphony: Understanding Its Influence on Communication Systems in Nigeria

Obiyemi,

Moloi

2024

Rainfall - Observations and Modelling

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“…In addition, the benefits provided by HF bands are gaining the attention of industry and researchers [1][2][3][4][5]. HF bands are linked to a wide bandwidth, frequency reuse, short wavelength, interaction with atmospheric elements, and low foliage penetration, especially in dense green vegetation [6,7]. The introduction of 5G and, eventually, 6G technologies results in increased bandwidth, faster speeds, and lower latency of 1 millisecond or less.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks for Earth-Space Link Applications: A Prediction Approach and Intercomparison of Rain-influenced Attenuation Models

Ojo¹,

Ijomah²,

Akinpelu³

2022

IJISA

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The impact of rain-influenced attenuation (RIA) has a more pronounced effect as frequency increases, especially in the tropical zones with heavier rainfall than the temperate zones. The International Telecommunication Union (ITU) has recommended a universal model which may not fit well in this tropical region due to the temperate data used to develop the model. It is therefore necessary to adopt locally measured data to develop a suitable model for each region, as also recommended by ITU recommendation 618-13. The experimental site for this study is at the Department of Physics, Federal University of Technology, Akure, Nigeria (7.299° N, 5.147° E) in the tropical rainforest region of Nigeria. In the present work, the backpropagation neural network (BPNN) of the artificial neural network (ANN) is trained based on time-series rain rates data collected between 2015 and 2019 to predict time-series RIA. Based on four inputs (rain rate, rain heights, elevation angle, and polarization angle), the generated data was subjected to training, validation, and testing. The ANN was further trained using the Levenberg-Marquardt algorithm to fit the inputs and the targets to create a dynamic model for RIA forecasting. Further validation was tested using actual data of rain attenuation from a Ku-band beacon at the site. Subsequently, the RIA model created by the ANN was compared to those generated using the synthetic storm technique, ITU, and the actual rain attenuation obtained from a beacon measurement. The highest rain rate observed was about 225.8 mm/hr with a corresponding rain attenuation of about 61 dB as estimated by the SST model and about 68 dB by the ITU model, while the predicted attenuation by the ANN is 55 dB. This implies that an extra power of 6 dB and 13 dB is added by the SST model and ITU model, respectively, for the downlink signal, to compensate for the rain attenuation link. The results also reveal that during 0.01 percent of an average year that signal may be attenuated, a relatively tiny margin of error between anticipated rain attenuation using ANN and the SST model is exceeded. In general, the new ANN-generated RIA model had the lowest root mean square error, average relative error, and standard deviation at the selected time percentages, according to the model validation. Hence, the new ANN model can predict more effective RIA in the region when compared with the global ITU-R model.

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Analysis of Rain-Induced Attenuation at Ku-V Band Using Gamma Drop Size Distribution Model Over a Tropical Region

Cited by 2 publications

References 29 publications

Rainfall’s Symphony: Understanding Its Influence on Communication Systems in Nigeria

Rainfall’s Symphony: Understanding Its Influence on Communication Systems in Nigeria

Artificial Neural Networks for Earth-Space Link Applications: A Prediction Approach and Intercomparison of Rain-influenced Attenuation Models

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