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

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 Mahalanob...

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Published in:IEEE transactions on communications 2000-10, Vol.48 (10), p.1614-1617
Main Authors: Hart, B.D., Borah, D.K., Pasupathy, S.
Format: Article
Language:English
Subjects:
Additive noise
Bias
Channels
Detectors
Dispersion
Estimators
Euclidean distance
Fading
Filtering
Gaussian noise
Maximum likelihood detection
Maximum likelihood estimation
Noise
Preserving
Rayleigh channels
White noise
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Borah, D.K.
Pasupathy, S.
description 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.
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subjects Additive noise
Bias
Channels
Detectors
Dispersion
Estimators
Euclidean distance
Fading
Filtering
Gaussian noise
Maximum likelihood detection
Maximum likelihood estimation
Noise
Preserving
Rayleigh channels
White noise
title Autocovariance preserving estimator (APE) interpretation of the MLSD metric for Rayleigh fading channels
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