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2023-05-31
Variational Bayesian Learning for the Modelling of Indoor Broadband Powerline Communication Impulsive Noise
By
Progress In Electromagnetics Research B, Vol. 100, 109-131, 2023
Abstract
Powerline communication (PLC) noise is the main cause of reduced performance and reliability of the communication channel. The major source of these noise bursts, which distort and degrade the communication signal, is the arbitrary plugging in and unplugging of electric devices from the electrical network. It is therefore important to perform statistical modelling of the PLC noise characteristics to enable the development and optimisation of reliable PLC systems. This paper presents the Variational Bayesian (VB) Gaussian Mixture (GM) modelling of the amplitude distribution of the indoor broadband PLC noise. In the proposed model, a fully Bayesian treatment is employed where the parameters of the GM model are assumed to be random variables. Consequently, prior distributions over the parameters are introduced. The VB criterion is used to determine the optimal number of components where the Bayesian information criterion emerges as a limiting case. To find the parameters of the GM components, the variational-expectation maximisation algorithm is employed. Measurements from different indoor PLC environments are then used to validate the model. Thereafter, performance analysis is carried out, and the VB framework is compared to the Maximum Likelihood (ML) estimate method. It is observed that while the ML model performs better when the amplitude distribution contains multiple peaks, the VB framework offers high accuracy and good generalization to the measured data and is thus effective in modelling the amplitude distribution of the PLC noise.
Citation
Florence Chelangat, and Thomas Joachim Odhiambo Afullo, "Variational Bayesian Learning for the Modelling of Indoor Broadband Powerline Communication Impulsive Noise," Progress In Electromagnetics Research B, Vol. 100, 109-131, 2023.
doi:10.2528/PIERB23020808
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