The aim of our thesis is to propose and to evaluate the performances of some asynchronous impulsive noise mitigation techniques for transmission over indoor power lines. Indeed, besides the particular physical properties that characterize this transmission channel type, asynchronous impulsive noise remains the difficult constraint to overcome on power lines communications (PLC). Usually, the impact of asynchronous impulsive disturbances over power lines is partly compensated by means of retransmission mechanisms. However, the main drawbacks of the use of retransmission solutions for impulsive noise mitigation are the bitrate loss and the induced time delays that may be prohibitive for real-time services. Although several other countering strategies are proposed in the literature, only very few of them have a good compromise between correction capability and implementing complexity for PLC systems. In this context, we proposed an adaptive linear filter, the Widrow filter, also known as ADALINE (Adaptive LInear neurons), as a denoising method for PLC systems. To improve the performance of the denoising method using ADALINE, we proposed to use a neural network (NN) as a nonlinear denoising method. The neural network is a good generalization of the ADALINE filter. In a second step, to improve the performances of denoising by NN, we proposed a combined denoising method based on EMD (Empirical Mode Decomposition) and MLPNN (Multi Layer Perceptron Neural Network). The noised signal is pre-processed by EMD which decomposes it into signals less complex and therefore more easily analyzed. Then the MLPNN denoises it. Finally, we proposed an asynchronous impulsive noise estimation method using the GPOF method (Generalized Pencil Of Function). The performances of the two methods, EMD-MLPNN and GPOF technique, are evaluated using a PLC transmission chain compatible with the HPAV standard.
