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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
