Prediction of Rain Attenuation Series with Discretized Spectral Model

Jie Chen (1, 2) , Cédric Richard (1) , Paul Honeine (2) , Jean-Yves Tourneret (3, 4)

1 LAGRANGE - Joseph Louis LAGRANGE
2 LM2S - Laboratoire Modélisation et Sûreté des Systèmes
3 IRIT-SC - Signal et Communications
4 Toulouse INP - Institut National Polytechnique (Toulouse)

machine learning, adaptive filtering, Spectral model is simple and efficient for modeling the rain attenuation which occurs in satellite communication channels. The prediction of this attenuation series is a vital step for adaptive coding or adaptive power control, which can improve the efficiency of a communication system. In simulation tasks, the discretized spectral model is usually used for generating the attenuation sequence. Due to this reason, in this paper we derive the conditional probability distribution of the predicted attenuation based on the discretized spectral model. This predictor can be used as a bound for others linear or nonlinear predictor of this model.

Mots clés: Predictive models; Prediction algorithms; Least squares approximation; Computational modeling; Adaptation models; attenuation sequence; communication system; adaptive power control; adaptive coding; satellite communication channels; rain attenuation model; Rain; Attenuation; linear predictor; nonlinear predictor; conditional probability distribution; atmospheric techniques; rain attenuation series; discretized spectral model

Domaines: Apprentissage [cs.LG]; Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Jie Chen, Cédric Richard, Paul Honeine, Jean-Yves Tourneret. Prediction of Rain Attenuation Series with Discretized Spectral Model. Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2012, Munich, Germany. pp.2407-2410, ⟨10.1109/IGARSS.2012.6351006⟩. ⟨hal-01966022⟩