volume | PIER Journals

Abstract

Power line communication (PLC) channels are significantly affected by bursty and impulsive electromagnetic interference (EMI), whose temporal clustering and amplitude variability are not adequately captured by conventional statistical models. Existing approaches often treat impulse arrivals and amplitudes independently, limiting their ability to reproduce the coupled dynamics observed in practical transmission line environments. In this study, a marked self-exciting point process model is proposed for PLC noise characterization, where impulse arrival times are governed by a Hawkes-type process, and amplitudes are represented as stochastic marks. The framework incorporates mark-dependent excitation, allowing higher-amplitude impulses to influence subsequent event occurrences and thereby capture the observed bursty structure. A measurement-driven analysis shows that PLC impulsive noise exhibits short-term temporal dependence and deviation from memoryless behavior, as reflected in skewed inter-arrival distributions, nonlinear complementary cumulative distribution function (CCDF) characteristics, and non-zero autocorrelation at small lags. Parameter estimation indicates dominant baseline activity with moderate self-excitation and rapidly decaying temporal influence. Amplitude modeling revealed a skewed distribution with intermittent high-energy events, whereas extreme value analysis using a generalized Pareto distribution suggested a bounded tail behavior for threshold exceedances. A joint likelihood-based estimation framework was developed to enable practical parameter inference. Validation of measured PLC datasets demonstrated that the proposed model consistently captures both temporal and amplitude characteristics without parameter re-estimation. A comparative analysis with a Poisson model showed clear discrepancies, with a quantified CCDF error of 0.15932, highlighting the limitations of memoryless assumptions. Overall, the proposed marked self-exciting framework provides a physically interpretable and statistically consistent representation of impulsive EMI in PLC systems, offering a robust foundation for improved modeling and mitigation strategies.

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Dr. Steven O. Awino

Dr. Bakhe Nleya