Performance Improvement in Satellite Networks Based on Markovian Weather Prediction

Prediction of channel characteristics can be of immense value in improving the quality of signals in high frequency satellite systems. Making prediction of rainfall rate (RR) using Markov theory and using that prediction in an intelligent system (IS) to maintain the quality of service (QoS) in channels impacted by attenuation due to weather is the object of this paper. The paper describes the method of prediction rainfall rate using weather collected by environment agencies and applying the predictions to gateway and ground terminal for optimal control of channel characteristics. This novel method of predicting weather characteristics using Markov theory supplies valuable data to develop an enhanced back propagation-learning algorithm to iteratively tune the IS to adapt to changing weather conditions. The effectiveness of the algorithm was tested on a simulated model for activating the weighted modulation and codepoint control. It demonstrated marked improvements in channel parameter tuning and signal quality.