Support vector machine for channel prediction in high-speed railway communication systems - Details

author：

Dong, Zhixiang (Dong, Zhixiang.) ^[1] | Zhao, Yisheng (Zhao, Yisheng.) ^[2] (Scholars：赵宜升) | Chen, Zhonghui (Chen, Zhonghui.) ^[3] (Scholars：陈忠辉)

Indexed by：

EI Scopus

Abstract：

In　this　paper,　the　problem　of　fast　time-varying　channel　prediction　is　investigated　in　high-speed　railway　communication　systems.　A　channel　prediction　algorithm　is　proposed　based　on　a　support　vector　machine　(SVM)　model.　In　order　to　further　improve　the　prediction　accuracy,　the　penalty　coefficient　and　Gaussian　kernel　width　of　the　SVM　model　are　optimized　by　a　genetic　algorithm　(GA).　Simulation　results　show　that　the　proposed　prediction　model　based　on　both　the　SVM　and　the　GA　(SVM-GA)　has　lower　prediction　error　than　traditional　auto-regressive　(AR)　and　single　SVM　prediction　models.　In　addition,　when　the　effect　of　the　noise　on　prediction　performance　is　considered,　the　SVM-GA　prediction　model　is　superior　to　the　AR　and　the　SVM　models　in　terms　of　normalized　mean　squared　error.　©　2018　IEEE.

Keyword：

Forecasting Genetic algorithms Mean square error Predictive analytics Railroads Railroad transportation Support vector machines

Community：

[ 1 ] [Dong, Zhixiang]College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
[ 2 ] [Zhao, Yisheng]College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
[ 3 ] [Chen, Zhonghui]College of Physics and Information Engineering, Fuzhou University, Fuzhou, China