In this paper, novel statistical models for the representation of the periodic impulsive noise generated by power loads connected to power grids in indoor scenarios are developed. Their derivation is based on a set of experimental results acquired in a measurement campaign and on deseasonalized autoregressive moving average modeling of cyclostationary random processes. Numerical results are evidence that the proposed models can provide an accurate stochastic representation of the periodic impulsive noise generated by specific appliances in the 1–30 MHz band, at the price of limited computational complexity

In this paper, the impact of statistical noise modelling on the error performance achieved by orthogonal frequency-division multiplexing (OFDM) over indoor broadband power-line channels is investigated. Different classes of statistical models suitable to represent power-line noise are illustrated and their impact on the error performance of a specific OFDM system is assessed via computer simulations. Numerical results are compared with the error performance provided by the same system in the presence of measured power-line noise; this is evidence that a realistic indication of error performance can be achieved only if the power spectral density of the adopted noise model exhibits a good match with that of the measured noise. In practice, this result can be achieved by modeling the power-line noise as a moving average random process of proper order; however, a satisfying match can be achieved as well if other simple noise models available in the technical literature are adopted

Indoor powerline channels usually exhibit a cyclic input–output behavior due to the time-varying impedance of power loads. This makes typical time-invariant system models unsuitable to provide a faithful representation of such channels. In this paper, starting from the so-called Zadeh's series expansion, a discrete-time parametric representation of a linear periodically time-varying system is developed, and it is shown how a reduced-complexity version of it can be adopted to model indoor powerline channels. Then, various methods for estimating the parameters of the proposed representation are developed and compared in terms of performance and complexity. Numerical results evidence that our reduced complexity model is able to provide an accurate representation of indoor powerline channels and is of practical interest for both smart-grid applications and home area networks