Channel estimation in a DMT based power-line communication system using sparse Bayesian regression

Authors:
Ashraf A. Tahat;Nikolaos P. Galatsanos
Affiliations:
School of Electrical Engineering, Princess Sumaya University for Technology, Amman, Jordan;Electrical and Computer Engineering Department, University of Patras, Rio, Greece
Venue:
ROCOM'11/MUSP'11 Proceedings of the 11th WSEAS international conference on robotics, control and manufacturing technology, and 11th WSEAS international conference on Multimedia systems & signal processing
Year:
2011

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Abstract

An enhanced power-line communications channel estimation method in discrete multitone (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.