Extreme value theory based decision directed OFDM channel tracking

Decision directed channel tracking (DDCT) at high fade rates in OFDM based systems is addressed in this paper. Channel estimation in DDCT can be formulated as a linear errors-in-variables regression problem. While most of the errors in the regression matrix (equalization errors) are Gaussian in nature, few of the detected symbols can have high error due to the frequency and time selective fading. These poor symbol decisions behave like outliers in the regression matrix and give rise to contaminated Gaussian noise distributions. Classical estimators like total least squares (TLS) and the expectation-maximization (EM) based estimators exhibit poor performance in the presence of such outliers. We propose the Huber's M (HM) estimator and an extreme value theory (EVT) based M estimator for the DDCT problem. The proposed HM and EVT-HM estimators are robust to outliers and have an efficiency greater than 95% in purely Gaussian noise. The error rate performance of the proposed HM and EVT-HM estimators are compared with that of the TLS estimator, and the EM based estimator proposed in [4]. © 2006 IEEE.