Channel estimation and modeling of power line communication

This thesis deals with modeling of power line communication. A two-port network model is theoretically described. A substantial part is focused on the mathematical description of distribution network using the method, which uses chain parameter matrices describing the relation between input and output voltage and current of the twoport network. This method is used for modeling sample power line topology. Furthermore, taps length and taps impedance influence on the transfer functions for different topology are examined. In this thesis, a decision–directed method is proposed for channel estimation and equalization in Power line communication (PLC) based on orthogonal frequency division multiplexing (OFDM). This method does not require a priori knowledge on the power line. Simulations on a realistic indoor power-line system show that the method achieves very good channel estimation and equalization performances and that it is robust to impulsive noise and nonlinearities. Later multilayer perceptron (MLP) based algorithm called back propagation algorithm has been proposed in power line communication. The present method (back propagation algorithm) is a OFDM based model which exploited for the channel estimation. Simulations on a realistic indoor power-line system show that the results obtained from the channel estimation using present model are significantly improved when compared with competitive neural network. It is also noteworthy to mention that the computational complexity is decreased using the present algorithm