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Uniformly Improving Maximum-Likelihood SNR Estimation of Known Signals in Gaussian Channels
KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0001-6630-243X
KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0001-7182-9543
KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-7926-5081
2014 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 62, no 1, 156-167 p.Article in journal (Refereed) Published
Abstract [en]

The signal-to-noise ratio (SNR) estimation problem is considered for an amplitude modulated known signal in Gaussian noise. The benchmark method is the maximum-likelihood estimator (MLE), whose merits are well-documented in the literature. In this work, an affinely modified version of the MLE (AMMLE) that uniformly outperforms, over all SNR values, the traditional MLE in terms of the mean-square error (MSE) is obtained in closed-form. However, construction of an AMMLE whose MSE is lower, at every SNR, than the unbiased Cramer-Rao bound (UCRB), is shown to be infeasible. In light of this result, the AMMLE construction rule is modified to provision for an a priori known set, where the SNR lies, and the MSE enhancement target is pursued within. The latter is realized through proper extension of an existing framework, due to Eldar, which settles the design problem by solving a semidefinite program. The analysis is further extended to the general case of vector signal models. Numerical results show that the proposed design demonstrates enhancement of the MSE for all the considered cases.

Place, publisher, year, edition, pages
IEEE Signal Processing Society, 2014. Vol. 62, no 1, 156-167 p.
Keyword [en]
Bias, Cramer-Rao bound, maximum-likelihood, optimization, SNR
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-134806DOI: 10.1109/TSP.2013.2274638ISI: 000330291000013Scopus ID: 2-s2.0-84898424786OAI: oai:DiVA.org:kth-134806DiVA: diva2:668155
Funder
EU, FP7, Seventh Framework Programme, 228044
Note

QC 20140227. Updated from accepted to published.

Available from: 2013-11-29 Created: 2013-11-29 Last updated: 2017-12-06Bibliographically approved

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