Prediction method to maintain QoS in weather impacted wireless and satellite networks

Abstract

Rain and snow can have a distorting effect on Ku and Ka bands signal fidelity resulting in excessive digital transmission errors. This loss of signal attenuation is commonly referred to as rain fade. Rain fade impacts the Quality of Service in wireless and satellite networks. A decision support system is therefore necessary for service providers to accurately predict rain fade and establish mitigation planning by adaptively selecting appropriate power level, coding and modulation schemes. This paper establishes an important component of the decision support system by adaptively computing rainfall rate and rain attenuation at any location on earth using ITU-R models combined with bi-linear interpolation and frequency extrapolation. We also introduce a novel method for accurately determining rain attenuation as a function of frequency based on proceeding results from prediction weather database, since rain attenuation is considered a dominant impairment for wireless signals [1]–[6]. Finally, a three dimensional relationship is proposed for rain attenuation with both frequency and probability to provide decision support system with an accurate view of satellite’s parameters. The derived parameter values, being fed through neural networks and fuzzy systems to improve estimate Signal to Noise Ratio, will enable the decision support system to maintain Quality of Service and Service Level Agreements by adaptively adjusting satellite signal power, modulation, coding, and data rate at unpredictable weather conditions.