# Itu scintillation model-GISM (ionospheric model) - IEEA

Analysis and comparison model for measuring tropospheric scintillation intensity for Ku-band frequency in Malaysia. This study has been based on understanding local propagation signal data distribution characteristics an did entifying and predicting the overall impact of significant attenuating factors regarding the propagation path such as impaired propagation for a signal being transmitted. Predicting propagation impairment is important for accurate link budgeting, there by leading to better communication net work system designation. This study has thus used sample data for one year concerning beacon satellite operation in Malaysia from April to April This analysis showed that scintillation intensity distribution followed Gaussian distribution forlong-term data distribution.

The gamma model proposed by Karasawa et al. The Global Ionospheric Scintillation Model allows obtaining both mean errors and scintillations due to propagation through ionosphere. However, the ITU-R and Karasawa models have overestimation results over the measured data for smaller values of N wet. This tropospheric scintillation intensity study responded to the requirement for better understanding of propagation impairment in satellite communication systems. Salonen, andJ. The second one provides the scintillation effects. Comparison between the predicted Weman wearing thongs the measured scintillation intensity fade.

## Diaper help. GISM overview

STK will compute the loss in dB that will occur for the percentage time not exceeding the specified limit. Temperatures and humidity are high throughout the year. The pdf has a slightly greater spread in the tail lower kurtosis than a Gaussian distribution Itu scintillation model positive skew for Diffrent sex postions observational experimental data. The cumulative distribution of the fade tropospheric scintillation is given as. In general, signal fade caused by rain Itu scintillation model on communication signals is more significant compared to signal fade caused by tropospheric scintillation. Enter the Tropospheric Fade Outage as a percentage. Search all SpringerOpen articles Search. Fade depths for the average year Computes the Celebrity babies digitally aged depth exceeded for a percentage of time over an average year. The multipath fading and scintillation effect due to rain were considered. Software to generate basic transmission loss values and curves of Itu scintillation model. The data were extracted by passing through a fifth-order high-pass Butterworth filter with a 0. The models predict monthly mean logarithm of log-signal variance by scaling a normalized mean logarithm of log-signal variance with Marzano and D'Auria ; Marzano et al. Figure 8. The rain events were determined with the use of a rain gauge.

Environmental factors can affect the performance of a communications link or a radar system.

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Prediction model for the diurnal behavior of the tropospheric scintillation variance Abstract: Tropospheric scintillation is caused by variations of the refractive index due to turbulence. The only meteorological input parameter for two common current scintillation models by Karasawa et al.

Because clouds and cloud formation are closely associated with the turbulence, quantitative cloud parameters were looked for. Both diurnal and seasonal variations between scintillation variance and average amount of Cumulus type clouds are well correlated. This model is derived and tested using scintillation measurements at four sites in different climates in Finland, United Kingdom, Japan, and Texas.

Article :. Date of Publication: Sep DOI: Need Help?

Figure 1. The impact on frequency power exponent values obtained through measurement sites for these models utilizing large antennas and low elevation angles could have contributed to the over- or under-prediction of these models towards the measurement sites. Therefore, these models cannot be applied globally, especially to tropical countries like Bandung. In addition, it is of less interest to investigate scintillation under rainy conditions for low-availability satellite system design purposes because rain attenuation is usually much more pronounce than scintillation fades Marzano et al. The scintillation intensity for every model is calculated using the formulas provided above. Average sea level surface refractivity and average annual difference in values of refractivity.

### Itu scintillation model. Publications by Sector

STK will compute the loss in dB that will occur for the percentage time not exceeding the specified limit. Fade depths for the average year Computes the fade depth exceeded for a percentage of time over an average year. For example, fade depth will exceed the computed value for the climate of an average year as a percentage of the time over the year.

Fade depths for the average annual worst month Computes the fade depth exceeded for a percentage of time for the month with the worst climate over a year. Fade depth will exceed the computed value for a percentage of time over the worst month of the year. Models depolarization of a communications link due to rain. Enter the Tropospheric Fade Outage as a percentage. Computes the fade depth exceeded for a percentage of time over an average year.

Computes the fade depth exceeded for a percentage of time for the month with the worst climate over a year. Software, Data and Validation examples for ionospheric and tropospheric radio wave propagation and radio noise. Rollup Image. Page Content Page Content 6. Page Content 7. Page Content 2. Page Content 3. Page Content 4.

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A general purpose wide-range terrestrial propagation model in the frequency range 30 MHz to 50 GHz.

Analysis and comparison model for measuring tropospheric scintillation intensity for Ku-band frequency in Malaysia. This study has been based on understanding local propagation signal data distribution characteristics an did entifying and predicting the overall impact of significant attenuating factors regarding the propagation path such as impaired propagation for a signal being transmitted.

Predicting propagation impairment is important for accurate link budgeting, there by leading to better communication net work system designation. This study has thus used sample data for one year concerning beacon satellite operation in Malaysia from April to April This analysis showed that scintillation intensity distribution followed Gaussian distribution forlong-term data distribution.

A prediction model was then selected based on the above; Karasawa, ITU-R, Van de Kamp and Otung models were compared to obtain the best prediction model performance for selected data regarding specific meteorological conditions. This studys howed that the Karasaw a model had the best performance for predicting scintillation intensity for the selected data. Keywords: Tropospheric scintillation, Ku-band, satellite communication, atmospheric attenuation.

Radio-wave propagation through the Earths atmosphere has a major impact on system design; several propagation effects increase in importance when comparing lower frequency bands, having a high degree of accuracy and comprehensiveness concerning their prediction Agunlejika, et al.

Propagation impairment regarding satellite communication links, especially in the Ku band and signal level fluctuation caused by attenuation due to rain and tropospheric scintillation, must be is carefully considered to ensure accurate link budgeting.

Tropospheric scintillation concerns rapid signal amplitude and phase fluctuation throughout a satellite link. It is caused by irregularities and turbulence in the first few kilometres above the ground, therebyaffecting atmospheric refractive index measurement Mandeep et al. A link for propagation through the troposphere consists of combining random absorption andscattering from acontinuum ofsignals along apathcausing random amplitude and random scintillation in the waveform being received.

Scintillation effect varies as time elapses and is dependent upon frequency, elevation angle and weather conditions, especially dense cloud. The greatest effect caused by tropospheric scintillation is signal fading, thereby acting as a limiting factor on system performance Akhondi and Ghorbani, This is why accurate prediction is important when evaluating a link budget, especially in highly tropospheric scintillation conditions.

Scintillation occurs continuously, regardless of whether the sky is clear or rainy. When it is raining, signal level fluctuation known as scintillation can change together with rain attenuation affecting signal level. Signal log-amplitude level will rise dramatically and such extreme level data should be carefully eliminated Mandeep et al, The measurement of data collected from a beacon satellite having 12 GHz frequency, 2.

Disanayake et al, have mentioned that most available beacon data has been analysed regarding clear sky conditions and this essentially removes the bulk of low-attenuation-producing phenomena. Table 1 gives measurement site specifications. Signal attenuation due to rain is the most remarkable signal propagation effect in Ku-band frequency and this kind of loss due to the above can be greater than 15 dB over a short period of time Otung, All data which has become changed due to attenuation caused by rain is eliminated.

Considering a clear sky with or without rain , all data having a spike regarding extreme amplitude values due to rain attenuation has been removed by comparing it to rain gauge data values. Visual inspection was needed and performed for all data sequences to eliminate spurious and invalid data Garcia, Full attention must be paid during inspection to ensure obtaining accurate result from studies.

Scintillation variance values can be best described for scintillation intensity in the present study and have been calculated as the standard deviation of signal amplitude given in decibels dB. The model so selected depended on its correlation with wet refractivity index value, and meteorological conditions, i. Prediction model comparison was based on signal fading and enhancement. The chosen model was also able to predict long-term distribution propagation signals.

Karasawa has presented a prediction model for signal standard deviation regarding scintillation intensity as follows;. Concerning equation 1 , Karasawa obtained the following expression for scintillation enhancement:.

The long-term tropospheric scintillation prediction model proposed by the International Telecommunication Union-Radiocommunication sector ITU-R was used for calculating the standard deviation of signal fluctuation due to scintillation.

This model uses the wet term of earth refractivity wet N, regarding relative humidity and temperature, averaged at least once a month as input Agunlejika et al. This model is applicable for frequencies ranging from 7GHz to 20 GHz and 4 to 32 elevation angles. The following equation can be used for the ITU-Rprediction model;. Referring to equation 6, scintillation fading can be calculated from the following equation for 0.

No prediction model has been recommended by the ITU-Rfor scintillation enhancement. Scintillation standard deviation for long-term distribution can be estimated from the equation given below;. The percentage of time for scintillation intensity can be identified from the above equation, as in equations 11 and Figure 1 shows monthly cumulative distribution for scintillation variance considering average standard deviation of scintillation intensity over a one-month time period.

Such variance was determined by considering clear sky conditions without rain. Percentage time value was lower than scintillation variance value for April and that for April was slightly higher than for the other month.

Figure 2 shows that average monthly scintillation distribution followed gamma distribution for long-term distribution data collection. Long-term distribution data should be analysed more than once a month while only a few minutes are needed for short-term data analysis. Figure 2 gives values regarding negative state for signal level enhancement while correct or positive state is for signal level fading.

It obviously shows that variation in variance scintillation value for fading and enhancement was not equally likely. Signal fading had a long tail compared to enhancement and the shape was not symmetrical, as has been mentioned by Van de Kamp Fading and enhancement represent two types of scintillation signal level. Both have the irown use and functionalities which can have a large effect on the propagation of a signal being transmitted through the atmosphere.

When propagation signals are affected by rain, especially during the raining season, fading value will suffer a drastic change due to changes in signal amplitude. However, the enhancement value is not affected by rain or can become negligible. Figure 3 represents cumulative distribution for signal level fading and enhancement. Variance distribution for fading was slightly higher when comparing enhancement value for the lower percentage of time.

Such cumulative distribution was for a local data study with specific meteorological conditions due to geographical conditions. Prediction model selection was based on their relationship to meteorological conditions. Figure 4 shows that the Karasawa model gave good prediction, having 0.

The Karasawa was thus a suitable model for predicting local data regarding scintillation intensity for signal fading compared to the other models while the Otung model did not perform well in predicting scintillation data 0. However, only three models performed well regarding signal enhancement, as shown in Figure 5.

This comparison obviously showed that the Karasawa model also performed well for predicting signal level enhancement regarding scintillation data intensity. A small difference regarding variance value with 0. The Otung model was the worst model 0. Tropospheric scintillation predication models have been reviewed and evaluated, including models for predicting signal log-amplitude cumulative distribution and models for predicting scintillation intensity. This tropospheric scintillation intensity study responded to the requirement for better understanding of propagation impairment in satellite communication systems.

Better understanding can produce better system design. This study thus concluded that the Karasawa prediction model can be best used for predicting overall propagation impairment regarding scintillation on the Malaysian propagation path.

Agunlejika, O. Raji and O. Adeleke Ghorban Allnutt and F. Haidara Yamada and J. Allnutt "A new prediction method for tropospheric scintillation on earth-space paths" IEEETransaction on Antenna and Propagation, 36, 11, - H Syed,, I. Kiyoshi, T. Kenji and I. Mitsuyoshi KTervonen, E. Salonen, andJ. P Baptista,. Services on Demand Article. English pdf Article in xml format Article references How to cite this article Automatic translation Send this article by e-mail.

Introduction Radio-wave propagation through the Earths atmosphere has a major impact on system design; several propagation effects increase in importance when comparing lower frequency bands, having a high degree of accuracy and comprehensiveness concerning their prediction Agunlejika, et al.

Data analysis The measurement of data collected from a beacon satellite having 12 GHz frequency, 2. Scintillation standard deviation for long-term distribution can be estimated from the equation given below; The percentage of time for scintillation intensity can be identified from the above equation, as in equations 11 and Signal fading and enhancement can be determined as follows: The Otung model This model is similar to the ITU-R model, except for elevation angle dependent value which is sin 9 12 and this is shown as equation 15; Hence, fading and enhancement for signal level can be determined by using this equation: The analysis and comparison model Figure 1 shows monthly cumulative distribution for scintillation variance considering average standard deviation of scintillation intensity over a one-month time period.

Conclusions Tropospheric scintillation predication models have been reviewed and evaluated, including models for predicting signal log-amplitude cumulative distribution and models for predicting scintillation intensity. References Agunlejika, O.

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