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. 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. The implementation results of proposed model were also compared with the ITU-R model.

随着对移动宽带通信技术容量需求的每年成倍增长，无线运营商必须为未来几年移动通信量的巨大增长做好准备。它迫使科学家和研究人员提出具有支持更高数据速率能力的新无线频谱带。像毫米波这样的较高频谱带是此类问题的候选频带。由于存在气体，水蒸气和其他天气现象（如雨，暴风雪，冰雹，冰雹等），该频段面临信号无线电波衰减的挑战。为了预测由于雨水而引起的衰减，提出了不同的模型。 ITU-R模型是广泛接受的模型。ITU-R模型包含用于计算回归系数的复杂方法，该系数取决于频率和极化。本文采用K-Means算法提出了一种改进的ITU-R模型。提出的模型可以弥补ITU-R模型的不足，无法确定频率范围内的破裂点，并且已经通过机器学习算法对获得的软簇进行了训练，然后提出了数学模型来预测由于雨水造成的无线电波衰减。拟议模型的实施结果也与ITU-R模型进行了比较。提出的模型可以弥补ITU-R模型的不足，无法确定频率范围内的破裂点，并且已经通过机器学习算法对获得的软簇进行了训练，然后提出了数学模型来预测由于雨水造成的无线电波衰减。拟议模型的实施结果也与ITU-R模型进行了比较。提出的模型可以弥补ITU-R模型的不足，无法确定频率范围内的破裂点，并且已经通过机器学习算法对获得的软簇进行了训练，然后提出了数学模型来预测由于雨水造成的无线电波衰减。拟议模型的实施结果也与ITU-R模型进行了比较。