%0 Journal Article
%A DU Fei
%A GENG Sui-yan
%A SUN Ning-yao
%A ZHANG Yu
%A ZHAO Xiong-wen
%T Time-Varying Channel Modeling Using Least Square Support Vector Machine
%D 2019
%R 10.13190/j.jbupt.2019-008
%J Journal of Beijing University of Posts and Telecommunications
%P 29-35
%V 42
%N 5
%X Based on 2.55 GHz urban microcellular multiple-input multiple-output (MIMO) channel measurement data, the least squares support vector machine (LS-SVM) method was applied on time-varying channel model. Specifically, a genetic algorithm (GA) based LS-SVM (GA+LS-SVM) model was established for channel parameter prediction. Based on GA+LS-SVM model, the time-varying channel parameters, such as delay spread, horizontal angle spread and vertical angle spread of receiver, were investigated and predicted accurately. Moreover, the GA+LS-SVM model was compared with back propagation neural network and traditional LS-SVM algorithms to verify the effectiveness of the algorithm. In summary, with limited amount of data the GA based LS-SVM model can better adapt to non-linear time-varying channel to realize the accurate prediction of nonlinear time-varying channel parameters.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2019-008