Weiss-Weinstein Bound for Data-Aided Carrier Estimation

This letter investigates Bayesian bounds on the mean-square error (MSE) applied to a data-aided carrier estimation problem. The presented bounds are derived from a covariance inequality principle: the so-called Weiss and Weinstein family. These bounds are of utmost interest to find the fundamental M...

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Bibliographic Details
Published in:IEEE signal processing letters 2007-04, Vol.14 (4), p.283-286
Main Author: Renaux, A.
Format: Article
Language:English
Subjects:
Approximation
Array signal processing
Bayesian methods
Carrier frequency estimation
Carriers
Closed-form solution
Computer Science
Digital communication
Digital signal processing
Engineering Sciences
Error analysis
Estimators
estimators performance
Exact solutions
Frequency estimation
Inequalities
Mathematical analysis
Maximum likelihood estimation
Random variables
Signal and Image Processing
Signal to noise ratio
Spectral analysis
Weiss and Weinstein family bounds
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Summary:This letter investigates Bayesian bounds on the mean-square error (MSE) applied to a data-aided carrier estimation problem. The presented bounds are derived from a covariance inequality principle: the so-called Weiss and Weinstein family. These bounds are of utmost interest to find the fundamental MSE limits of an estimator, even for critical scenarios (low signal-to-noise ratio and/or low number of observations). In a data-aided carrier estimation problem, a closed-form expression of the Weiss-Weinstein bound (WWB) that is known to be the tightest bound of the Weiss and Weinstein family is given. A comparison with the maximum likelihood estimator and the other bounds of the Weiss and Weinstein family is given. The WWB is shown to be an efficient tool to approximate this estimator's MSE and to predict the well-known threshold effect
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2006.887782