Conditional mean estimator for the gramian matrix of complex gaussian random variables

Frank A. Dietrich, Felix Hoffmann, Wolfgang Utschick

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

The problem of estimating and predicting the Gramian of a matrix (with rather general structure) of correlated complex Gaussian random variables is addressed. We propose its conditional mean estimator as the optimum Bayesian estimator for a quadratic risk function and present its mean square error (MSE) performance analysis. Numerical results for the example of linear pre-equalization in a wireless communications application show a significantly improved performance of the novel estimator compared to known approaches.

Original languageEnglish
Title of host publication2005 IEEE International Conference on Acoustics, Speech, and Signal Processing,ICASSP '05 - Proceedings - Audio and ElectroacousticsSignal Processing for Communication
PublisherInstitute of Electrical and Electronics Engineers Inc.
PagesIII1137-III1140
ISBN (Print)0780388747, 9780780388741
DOIs
StatePublished - 2005
Event2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Philadelphia, PA, United States
Duration: 18 Mar 200523 Mar 2005

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
VolumeIII
ISSN (Print)1520-6149

Conference

Conference2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05
Country/TerritoryUnited States
CityPhiladelphia, PA
Period18/03/0523/03/05

Fingerprint

Dive into the research topics of 'Conditional mean estimator for the gramian matrix of complex gaussian random variables'. Together they form a unique fingerprint.

Cite this