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

Abstract

Abstract. 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,. 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.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.

The ARIMA prediction model of rain-induced dynamic attenuation characteristics

“…Modelling results could be written as (15) and (16). 1 1 Fig. 3 is a forecasting results by the forecasting model given in this paper.…”

“…The analysis results are listed in Table 2. It can be concluded that the forecasting model of the real-time dynamic characteristics rain-induced attenuation is ARIMA (1,1,6). Modelling results could be written as (15) and (16).…”

“…In recent years, researchers have put forward many shortterm prediction models of rain-induced attenuation [1]- [5].…”

“…In order to evaluate the required speed of adaptive anti-fading system to track the attenuation changes, the adaptive antifading technology needs to study the real-time dynamic behaviour of rain-induced attenuation. Therefore, studying and forecasting the real-time dynamic characteristics of raininduced attenuation is very important for the development of adaptive anti-fading technology [1]- [3].…”

Short-time traffic flow prediction with ARIMA-GARCH model

“…ARIMA-GARCH model is implemented in many areas other than financial analysis for prediction and forecast purposes. It is used to predict electricity prices [17] and the rain attenuation level in Earth-to-Satellite links [18], both of them achieve good results. Some studies also apply the ARIMA-GARCH model to model and predict the computer network traffic, the results show that the model can capture prominent traffic characteristics and has better prediction accuracy [19]- [20].…”