The indoor low-voltage power line network is characterized by highly irregular interferences, where background noise coexists with bursty impulsive noise originating from household appliances and switching events. Traditional noise models, which are considered monofractal models, often fail to reproduce the clustering, intermittency, and long-range dependence seen in measurement data. In this paper, a Multifractal Random Walk (MRW) framework tailored for Power Line Communication (PLC) noise modelling is developed. MRW is a continuous time limit process based on discrete-time random walks with stochastic log-normal variance. As such, the formulated MRW framework introduces a stochastic volatility component that modulates Gaussian increments, thus generating heavy-tailed statistics and multifractal scaling laws which are consistent with the measured PLC noise data. Empirical validation is carried out through structure function analysis and covariance of log-amplitudes, both of which reveal scaling characteristics that align well with MRW-simulated predictions. This proposed model captures both the bursty nature and correlation structure of impulsive PLC noise more effectively as compared to the conventional monofractal approaches, thereby providing a mathematically grounded framework for accurate noise generation and the robust system-level performance evaluation of PLC networks.

The indoor low-voltage power line network is characterized by highly irregular interferences, where background noise coexists with bursty impulsive noise originating from household appliances and switching events. Traditional noise models, which are considered monofractal models, often fail to reproduce the clustering, intermittency, and long-range dependence seen in measurement data. In this paper, a Multifractal Random Walk (MRW) framework tailored for Power Line Communication (PLC) noise modelling is developed. MRW is a continuous time limit process based on discrete time random walks with stochastic log-normal variance. As such, the formulated MRW framework introduces a stochastic volatility component that modulates Gaussian increments, thus generating heavy-tailed statistics and multifractal scaling laws which are consistent with the measured PLC noise data. Empirical validation is done through structure function analysis and covariance of log-amplitudes, both of which reveal scaling characteristics that align well with MRW simulated predictions. This proposed model captures both the bursty nature and correlation structure of impulsive PLC noise more effectively as compared to the conventional monofractal approaches, thereby providing a mathematically grounded framework for accurate noise generation and robust system-level performance evaluation of PLC networks.

Impulsive noise significantly degrades the performance of power line communication (PLC) systems due to their non-Gaussian amplitude distribution, burst clustering, and inherent temporal dependence. Conventional statistical and spectral models often describe marginal behavior but do not fully account for the underlying temporal organization of such noise processes. This paper introduces an ensemble-based non-linear dynamical framework for the short-window characterization of impulsive PLC noise using delay-embedded phase-space reconstruction (PSR). Rather than relying on extended stationary recordings, the analysis is conducted across multiple independent short-duration acquisition windows obtained from indoor low-voltage networks. For each realization, the delay parameter is selected using average mutual information, and the embedding dimension is determined through the false nearest neighbors (FNN) criterion. The reconstructed trajectories are then examined using correlation dimension estimation, largest Lyapunov exponent analysis, and recurrence quantification measures. The resulting non-linear descriptors reveal structured phase-space organization and low-dimensional dynamical characteristics that are not readily observable in the original time-domain representation. In addition, these findings show that short-window PLC data preserve meaningful dynamical characteristics and support the use of non-linear geometric descriptors for impulsive PLC noise analysis and future mitigation approaches.