Title :
Minimum mean-squared error covariance shaping
Author :
Eldar, Yonina C.
Author_Institution :
Dept. of Electr. Eng., Technion-Israel Inst. of Technol., Haifa, Israel
Abstract :
The paper develops and explores applications of a linear shaping transformation that minimizes the mean squared error (MSE) between the original and shaped data, i.e., that results in an output vector with the desired covariance that is as close as possible to the input, in an MSE sense. Three applications of minimum MSE shaping are considered, specifically matched filter detection, multiuser detection and linear least-squares parameter estimation.
Keywords :
covariance matrices; least mean squares methods; matched filters; minimisation; multiuser detection; parameter estimation; vectors; covariance matrices; data vector covariance; linear least-squares parameter estimation; linear shaping transformation; matched filter detection; minimum mean-squared error covariance shaping; multiuser detection; weighting matrix; Covariance matrix; Detectors; Matched filters; Multiuser detection; Parameter estimation; Process control; Shape control; Spectral shape; Vectors;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on
Print_ISBN :
0-7803-7663-3
DOI :
10.1109/ICASSP.2003.1201781
