A robust Bayesian inference-based channel estimation in power line communication systems contaminated by impulsive noise

Broadband power line communication based on an orthogonal frequency division multiplexing scheme is considered in this paper. In addition to multipath fading and frequency selectivity, the power line communication channel is inuenced by impulsive noise. More bit errors in power line communications are caused by these effects. In this paper, a relevance vector machine (R VM) is applied to the received complex data in order to estimate the power line communication channel impulse response in a baseband model. The bit error rate and mean squared error (MSE) performances under the impulsive noise and multipath effects are analyzed. It is shown that introducing a new kernel can increase the robustness of the proposed method against the critical effects of impulsive noise and multipath with low complexity. It is also shown that applying proper values to hyperparameters based on the minimum mean square error criterion can increase convergence speed and decrease MSE in our proposed method. Experimental results show the better performance of proposed algorithm compared to recently reported methods such as the Huang method and conventional R VM with Gaussian kernel function in complex mode, namely complex R VM.