Sensitivity-Informed Bayesian Inference for Home PLC Network Models with Unknown Parameters

• Main Authors: David S. Ching, Cosmin Safta, Thomas A. Reichardt
• Format: Article
• Language: English
• Published: MDPI AG 2021-04-01
• Series: Energies
• Subjects: power line communications (PLC); Bayesian inference; Transitional Markov Chain Monte Carlo; channel calibration; home network
• Online Access: https://www.mdpi.com/1996-1073/14/9/2402

Bayesian inference is used to calibrate a bottom-up home PLC network model with unknown loads and wires at frequencies up to 30 MHz. A network topology with over 50 parameters is calibrated using global sensitivity analysis and transitional Markov Chain Monte Carlo (TMCMC). The sensitivity-informed Bayesian inference computes Sobol indices for each network parameter and applies TMCMC to calibrate the most sensitive parameters for a given network topology. A greedy random search with TMCMC is used to refine the discrete random variables of the network. This results in a model that can accurately compute the transfer function despite noisy training data and a high dimensional parameter space. The model is able to infer some parameters of the network used to produce the training data, and accurately computes the transfer function under extrapolative scenarios.