Relevance vector machines for DMT based systems

Abstract

In this paper, an improved channel estimation method in discrete multi-tone (DMT) communication systems based on sparse Bayesian learning relevance vector machine (RVM) method is presented. The Bayesian frame work can obtain sparse solutions to regression tasks utilizing models linear in parameters. By exploiting a probabilistic Bayesian learning framework, the sparse Bayesian learning provides the accurate model for estimation and consequently equalization. We consider frequency domain equalization (FEQ) using the improved channel estimate at both the transmitter and receiver and compare the resulting bit error rate (BER) performance curves for both approaches and various techniques. Simulation results show that the performance of the RVM method is superior to the traditional least squares technique.