© The Institution of Engineering and Technology
This study is to construct the autoregressive models for the lowvoltage broadband power line communication (PLC) channel noise by two machine learning algorithms, namely the least square support vector machine (LSSVM) and wavelet neural networks. The main work is to compare the two classical machine learning algorithms and also compare them with the traditional Markovian–Gaussian method. To verify their availability and ability to adapt to the timevariant PLC channels, noise measurements for lowvoltage PLC channels in indoor and outdoor scenarios are carried out. The accuracy and efficiency of the two models are studied and compared based on a large amount of measurement data. The results show that both of the noise models can simulate and adapt to the timevariant lowvoltage broadband PLC channels very well. The LSSVM model is found to have shorter simulation time and higher accuracy. Moreover, the proposed noise models are also compared with the traditional Markovian–Gaussian model. The results show that both the proposed noise models exhibit higher accuracy and lower complexity, especially that the LSSVM is more appropriate to be applied as a noise generator in PLC link and network level simulations instead of the current Markovian–Gaussian model.
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