A new SVM-based modeling method of cabin path loss prediction

Abstract

A new modeling method of cabin path loss prediction based on support vector machine (SVM) is proposed in this paper. The method is trained with the path loss values of measured points inside the cabin and can be used to predict the path loss values of the unmeasured points. The experimental results demonstrate that our modeling method is more accurate than the curve fitting method. This SVM-based path loss prediction method makes the prediction much easier and more accurate, which covers performance traditional methods in the channel propagation modeling. © 2013 Xiaonan Zhao et al.