Markov Model Parameters Optimization for Asynchronous Impulsive Noise over Power Line Communication Network

The asynchronous impulsive noise is one of the most crucial factors that degrade the performance of power line communication (PLC) network. Several approaches have been presented to characterize the time domain behavior of the asynchronous impulsive noise over the PLC network. Among them a partitioned Markov-chains model has been validated with measured data. However, its model parameters estimation using the Simplex method can easily trap the final solution into the local optimum. To overcome this difficulty, a new estimation scheme based on the genetic algorithm (GA) is proposed in this paper. Experimental results show that the proposed scheme yields estimates which more closely match the experimental data statistics.