Short-term prediction of rain attenuation level and volatility in Earth-to-Satellite links at EHF band

Other literature type, Article English OPEN
L. de Montera ; C. Mallet ; L. Barthès ; P. Golé (2008)
  • Publisher: Copernicus Publications
  • Journal: Nonlinear Processes in Geophysics (issn: 1023-5809, eissn: 1607-7946)
  • Related identifiers: doi: 10.5194/npg-15-631-2008
  • Subject: Geophysics. Cosmic physics | Q | [ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing | Science | Physics | [ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing | QC1-999 | QC801-809 | [ SPI.ELEC ] Engineering Sciences [physics]/Electromagnetism

This paper shows how nonlinear models originally developed in the finance field can be used to predict rain attenuation level and volatility in Earth-to-Satellite links operating at the Extremely High Frequencies band (EHF, 20–50 GHz). A common approach to solving this problem is to consider that the prediction error corresponds only to scintillations, whose variance is assumed to be constant. Nevertheless, this assumption does not seem to be realistic because of the heteroscedasticity of error time series: the variance of the prediction error is found to be time-varying and has to be modeled. Since rain attenuation time series behave similarly to certain stocks or foreign exchange rates, a switching ARIMA/GARCH model was implemented. The originality of this model is that not only the attenuation level, but also the error conditional distribution are predicted. It allows an accurate upper-bound of the future attenuation to be estimated in real time that minimizes the cost of Fade Mitigation Techniques (FMT) and therefore enables the communication system to reach a high percentage of availability. The performance of the switching ARIMA/GARCH model was estimated using a measurement database of the Olympus satellite 20/30 GHz beacons and this model is shown to outperform significantly other existing models. <br><br> The model also includes frequency scaling from the downlink frequency to the uplink frequency. The attenuation effects (gases, clouds and rain) are first separated with a neural network and then scaled using specific scaling factors. As to the resulting uplink prediction error, the error contribution of the frequency scaling step is shown to be larger than that of the downlink prediction, indicating that further study should focus on improving the accuracy of the scaling factor.
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