Design of a Neural Network Based Impulsive Noise Detector: with Application to Improve Iterative OFDM-based PLC Receivers

The Impulsive noise (IN) in power-line communications (PLC) system affect the performance of system. We can transform the received signal into frequency domain, and hence the receiver can reconstruct the noise from received signal. However, finding the IN from the reconstructed noise is a non-linear problem, using a linear filter to handle this kind of problem would reduce the performance of the system. In this paper, we present a Neural Network based approach to detect IN in orthogonal frequency division multiplexing (OFDM) based baseband power-line communications system. Neural Network is a non-linear model and is suitable for the detection of IN; moreover, it is a self organizing system, which means that an Neural Network can adapt itself when the transmission environments change over time. The detection mechanism works in an iterative receiver that contains a pre-IN mitigation and a post-IN mitigation. The pre-IN mitigation is meant to null the stronger portion of IN, while the post-IN mitigation suppress the residual portion of IN by using an iterative process. We also present a target generator to train the Neural Network based IN detector in the training period. Simulation results show that the proposed Neural Network based IN detector can separate the IN from reconstructed noise, and hence improves PLC system performance.