Autocovariance preserving estimator (APE) interpretation of the MLSD metric for Rayleigh fading channels

Abstract

This paper describes a new interpretation of the maximum-likelihood sequence detector metric for a linearly modulated signal and an unknown time-varying, dispersive, Rayleigh fading channel. The metric, which involves the channel and noise autocovariances, is transformed into the intuitive Mahalanobis distance (squared Euclidean distance in white noise) between the observations and a reference estimated from them. A novel estimator, called the autocovariance preserving estimator, is obtained that trades off increased estimation bias for reduced sequence-error probability.