Channel Estimation in a DMT Based Power-Line Communication System Using Sparse Bayesian Regression
نویسنده
چکیده
An enhanced power-line communications channel estimation method in discrete multi-tone (DMT) communication system based on sparse Bayesian regression is presented. By exploiting a probabilistic Bayesian learning framework, the sparse model used provides an accurate model for channel estimation in presence of noise and consequently equalization. We consider frequency domain equalization (FEQ) using the improved channel estimate at both the transmitter and receiver for a power-line system and compare the resulting bit error rate (BER) performance curves for both approaches and various channel estimation techniques. Simulation results show that the performance of the proposed method is superior to previous least squares based techniques. Key-Words: SBL, DMT, RVM, Channel estimation, Regression.
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