查看文章

This paper proposes and discusses Autoregressive Moving Average (ARMA), Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) time series models for broadband power line communication (PLC) networks with impulsive noise enviroment in the frequency range of 1 – 30 MHz. In time series modelling and analysis, time series models are fitted to the acquired time series describing the system for purposes which include simulation, forecasting, trend assessment, and a better understanding of the dynamics of the impulsive noise in PLC systems. Also, because the acquired impulsive noise measurement data are observations made over time, time series models constitute important statistical tools for use in solving the problem of impulsive noise modelling and forecasting in PLC. In fact, the time series and other statistical methods presented in …