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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Bibliographic Details
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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Summary: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.
ISSN:0090-6778
1558-0857
DOI:10.1109/26.871383