Prediction of Rain Attenuation Series with Discretized Spectral Model
Résumé
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
| Origine | Fichiers produits par l'(les) auteur(s) |
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