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Abstract: Increasing demand of bandwidth in communication satellites has forced satellite links to be designed in Ku bands and above. But at these frequencies, rain and other tropospheric elements result in large attenuation. To mitigate the tropospheric attenuation of microwave satellite signals above 10GHz using any standard Fade Mitigation Technique (FMT), it is essential to have a priori knowledge about the level of attenuation. Hence, short-term rain attenuation prediction models play a key role in maintaining the link in which necessary compensation can be applied depending on the early information of attenuation. This paper presents a method of attenuation prediction using Adaptive Artificial Neural Network. Here In situ Learning Algorithm (ILA) has been used to enable the system to track the non-stationary nature of the attenuation. To validate this, Ku Band data, collected at three different sites in India have been used for the purpose of prediction. The performance of the algorithm is determined through the estimation of prediction accuracy by comparing the predicted values with the measured data. Results obtained using the mentioned technique shows considerably good accuracy even up to 20s of prediction interval with acceptable ratio between the under and over predictions. The prediction performance is evaluated for different prediction intervals. Furthermore the present model is also compared with the persistence model and the relative performance is quantified. [Copyright &y& Elsevier]

Abstract: The Ionospheric Total Electron Content is responsible for the group delay of the signals from the Navigation satellites. This delay causes ranging error, which in turn degrades the accuracy of position estimated by the receivers. For critical applications, single frequency receivers resort to Satellite Based Augmentation Systems in order to have improved accuracy and integrity. The performance of these systems in terms of accuracy can be improved if predictions of the delays are available simultaneously with real measurements. This paper attempts to predict the Total Electron Content using adaptive recurrent Neural Network at three different locations of India. These locations are selected at the magnetic equator, at the equatorial anomaly crest and outside the anomaly range, respectively. In-situ Learning Algorithm has been used for tracking the non-stationary nature of the variation. Prediction is done for different prediction intervals. It is observed that, for each case, the mean and root mean square values of prediction errors remain small enough for all practical applications. Analysis of Variance is also done on the results. [Copyright &y& Elsevier]