Abstract

AWINO, S. O.; AFULLO, T. J. O.; MOSALAOSI, M.  and  AKUON, P. O.. Time series analysis of impulsive noise in power line communication (PLC) networks. SAIEE ARJ [online]. 2018, vol.109, n.4, pp.237-249. ISSN 1991-1696.

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 numerous available literature draw upon research developments from two areas of environmetrics called stochastic hydrology and statistical water quality modelling as well as research contributions from the field of statistics. In time series modelling and analysis, we determine the most appropriate stochastic or time series model to fit our acquired data set at the confirmatory data analysis stage. No matter what type of stochastic model is to be fitted to the data set, we follow the identification, estimation, and diagnostic check stages of model construction. In addition, we explore the resulting autocorrelation functions in estimating the parameters of the selected time series models. Finally, SARIMA model is found suitable for computer-based PLC systems simulations and forecasting based on the diagnostic checks.

Keywords : PLC; power line network; impulsive noise; ARMA models; ARIMA models; SARIMA models; autocorrelation function.

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