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Journal of Electrical and Computer Engineering
Volume 2010, Article ID 191808, 8 pages
Research Article

Relevance Vector Machines for Enhanced BER Probability in DMT-Based Systems

1School of Electrical Engineering, Princess Sumaya University for Technology, Amman 11941, Jordan
2Electrical and Computer Engineering Department, University of Patras, Rio 26500, Greece

Received 15 December 2009; Revised 15 March 2010; Accepted 27 April 2010

Academic Editor: Cyril Leung

Copyright © 2010 Ashraf A. Tahat and Nikolaos P. Galatsanos. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A new channel estimation method for discrete multitone (DMT) communication system based on sparse Bayesian learning relevance vector machine (RVM) method is presented. The Bayesian frame work is used to obtain sparse solutions for regression tasks with linear models. By exploiting a probabilistic Bayesian learning framework, sparse Bayesian learning provides accurate models for estimation and consequently equalization. We consider frequency domain equalization (FEQ) using the proposed channel estimate at both the transmitter (preequalization) and receiver (postequalization) and compare the resulting bit error rate (BER) performance curves for both approaches and various channel estimation techniques. Simulation results show that the proposed RVM-based method is superior to the traditional least squares technique.