Approximations for ITV Rain Model Using Machine Learning

Abstract

In communication technologies, availability is the key performance matrix. Different factors which affect the availability of links are hardware reliability, finding interference etc. In radio wave propagation studies, attenuation caused by hydrometeors like rain plays an important role especially for higher frequency bands. Different models are there for the prediction of attenuation caused by rain out of which ITU-R model is one of the widely acceptable models. In this paper, K-Means algorithm is used to propose an improved ITU-R model. Proposed model can make up the shortcoming of ITU-R model to determine the break-up points in frequency range and obtained soft clusters have been trained by machine learning algorithms then proposes a mathematical model for prediction of radio wave attenuation due to rain. Results from proposed model compared with ITU-R model.