Autors: Kumar V., Singh H., Saxena K., Bonev, B. G., Prasad R.
Title: Soft Clustering for Enhancing ITU Rain Model based on Machine Learning Techniques
Keywords: Clustering; ITU model; Millimeter waves; Rain attenuation; Regression analysis; Satellite communication

Abstract: With the many folds increase in demand for capacity in mobile broadband communication technology every year, wireless carriers must be prepared for the tremendous increase in mobile traffic in coming years. It forces scientists and researchers to come up with new wireless spectrum bands which has capabilities to support higher data rates. The higher spectrum bands like millimeter waves are the candidate band for this type of problems. This band comes with the challenges of radio wave attenuations oof signals due to the presence of gases, water vapor and other weather phenomenon like rain, storms, snow, hail etc. Different models are presented in order to predict attenuation due to rain out of which ITU-R model is the widely acceptable model. The ITU-R model contains complex methodology for calculating regression coefficients which are depends on frequency and polarization. In this paper, K-Means algorithm is used to propose an improved ITU-R model.



    Wireless Personal Communications, vol. 1, issue 120, pp. 287-305, 2021, Netherlands, Springer Nature, ISSN 0929-6212

    Copyright Springer

    Вид: статия в списание, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus