L-PLC Channel Characteristics Prediction Based on SVM

Time series prediction can be a very useful tool in communication to predict the behavior of system. In this paper, support vector machine (SVM) is applied to predict the channel characteristics of low-voltage power line communication (L-PLC), which is a vast infrastructure of power distribution and offers an alternative and cost-effective Internet access technology. Firstly, the largest Lyapunov exponent and the saturated correlation dimension of time series measured from power line are calculated. According to the results, the L-PLC channel is manifested to be a chaotic system. Then, a prediction model of L-PLC channel characteristics is proposed based on phase space reconstruction theory and SVM algorithm. In this paper, the parameters of SVM algorithm are chosen by the minimum mean square error (MSE) principle and the selection principles of parameters are particularly discussed. The actual time series are used in experimental simulations. The simulation results indicate that the prediction model based on SVM can be used to predict L-PLC channel characteristics accurately. In addition, the relations among sampling interval, predicted step and prediction precision are discussed.