Prediction of Jakes Fading Channels Based on Least Squares Support Vector Machines

In this paper the LS-SVM is used to resolve the problem of fading channel prediction, thus a novel prediction algorithm is proposed. In this algorithm, the learning samples are constructed by the observed values of fading channel coefficients, and the prediction is implemented by resorting to the learning and decision ability of the LS-SVM. Performance evaluation of the proposed algorithm is carried out on Jakes fading channels. The results illustrate the feasibility and efficiency of the algorithm. Additionally, the experiments discuss the influences of embedding dimension on the prediction accuracy. The method of optimal embedding dimension selection is given.