Analysis of Time Diversity Gain for Satellite Communication Link based on Ku-Band Rain Attenuation Data Measured in Malaysia

Radio waves are attenuated by atmospheric phenomena such as snow, rain, dust, clouds, and ice, which absorb radio signals. Signal attenuation becomes more severe at extremely high frequencies, usually above 10 GHz. In typical equatorial and tropical locations, rain attenuation is more prevalent. Some established research works have attempted to provide state-of-the-art reviews on modeling and analysis of rain attenuation in the context of extremely high frequencies. However, the existing review works conducted over three decades (1990 to 2022), have not adequately provided comprehensive taxonomies for each method of rain attenuation modeling to expose the trends and possible future research directions. Also, taxonomies of the methods of model validation and regional developmental efforts on rain attenuation modeling have not been explicitly highlighted in the literature. To address these gaps, this paper conducted an extensive literature survey on rain attenuation modeling, methods of analyses, and model validation techniques, leveraging the ITU-R regional categorizations. Specifically, taxonomies in different rain attenuation modeling and analysis areas are extensively discussed. Key findings from the detailed survey have shown that many open research questions, challenges, and applications could open up new research frontiers, leading to novel findings in rain attenuation. Finally, this study is expected to be reference material for the design and analysis of rain attenuation.

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“…2018 [205] To mitigate rain attenuation for 12.255 GHz earth-to-satellite link using time diversity technique in Malaysia.…”

Section: Ref Objective Of Study Methodology Adoptedmentioning

confidence: 99%

A Review on Rain Signal Attenuation Modeling, Analysis and Validation Techniques: Advances, Challenges and Future Direction

et al. 2022

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“…A flow rate sensor and a water level sensor were utilized in the process of data acquisition while prediction model was done to make a system that can predict flood occurrence. Researchers have used statistical means to analyse flood data in [5,7] while the use of ANN and other hybrid system integrating fuzzy system and neural network have been heavily explored in [8][9][10][11][12][13][14]. The use of Boosted regression tree for analysis was carried out in [8] to analyze reservoir inflow.…”

Section: Introductionmentioning

confidence: 99%

“…This approach is mandatory in order to find the best alternative to train system to be effective in detecting annual monsoonal floods. [13] accessed satellite data in order to reduce attenuation caused by rainfall. Additionally, this gave an insight in the means that flood prediction data can be useful in aiding link reliability in Earth-space satellite communication.…”

Section: Introductionmentioning

confidence: 99%

Intelligent flood disaster warning on the fly: developing IoT-based management platform and using 2-class neural network to predict flood status

2019

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The number of natural disasters occurring yearly is increasing at an alarming rate which has caused a great concern over the well-being of human lives and economy sustenance. The rainfall pattern has also been affected and this has caused immense amount of flood cases in recent times. Flood disasters are damaging to economy and human lives. Yearly, millions of people are affected by floods in Asia alone. This has brought the attention of the government to develop a flood forecasting method to reduce flood casualties. In this article, a flood mitigation method will be evaluated which incorporates a miniaturized flow, water level sensor and pressure gauge. The data from the two sensors are used to predict flood status using a 2-class neural network. Real-time monitoring of the data from the sensor into Thingspeak channel were possible with the use of NodeMCU ESP8266. Furthermore, Microsoft’s Azure Machine Learning (AzureML) has built-in 2-class neural network which was used to predict flood status according to predefine rule. The prediction model has been published as Web services through AzureML service and it enables prediction as new data are available. The experimental result showed that using 3 hidden layers has the highest accuracy of 98.9% and precision of 100% when 2-class neural network is used.

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Prediction of Time Diversity Gain for Earth-to-Satellite Microwave Link Design Based on Real Time Rain Intensity Measurement

et al. 2023

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The propagation impairments by rain strongly affect the earth-to-satellite links operating at frequencies higher than 18 GHz mainly due to the severe performance degradation of satellite communication system. The time diversity technique with appropriate time delay between successive transmissions has been found effective in mitigating the rain fade. However, time diversity analysis demands measured rain attenuation data, where the latter is unavailable at most of the places to be able to design future high frequency links. The time diversity gain prediction model has been found to be robust for time diversity improvement. Here, we propose a new model for the rain rate with and without the time delay considering the three variables of rain rate, time delay and frequency. The rain rate and time delay functions were first used to derive the constants by the regression from the rain rate and rain rate gain. The constant for the frequency function was then extracted from the cumulative distribution function of the attenuation predicted by the analytically obtained ITU-R and gain equations. The proposed model was validated using one-year rain rate and attenuation data measured at two different locations in Malaysia demonstrating a 7% prediction inaccuracy when compared to the existing models, therefore, it can be reliably used for future earth-tosatellite link designs by using measured rain rate at any higher frequencies.INDEX TERMS Rain rate, rain attenuation, time delay, time diversity technique.

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Analysis of Time Diversity Gain for Satellite Communication Link based on Ku-Band Rain Attenuation Data Measured in Malaysia

Cited by 5 publications

References 11 publications

A Review on Rain Signal Attenuation Modeling, Analysis and Validation Techniques: Advances, Challenges and Future Direction

A Review on Rain Signal Attenuation Modeling, Analysis and Validation Techniques: Advances, Challenges and Future Direction

Intelligent flood disaster warning on the fly: developing IoT-based management platform and using 2-class neural network to predict flood status

Prediction of Time Diversity Gain for Earth-to-Satellite Microwave Link Design Based on Real Time Rain Intensity Measurement

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