On reduced-rank approaches to matrix Wiener filters in MIMO systems

G. Dietl, W. Utschick

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

12 Scopus citations

Abstract

Reduced-rank processing is a well-known strategy in the reduction of computational complexity and performance enhancement in the case of low sample support. In this paper, we use the eigenspace based principal component (PC) and cross-spectral (CS) method for rank-reduction of a matrix Wiener filter (WF) which estimates a signal vector instead of a scalar by minimizing the mean square error. Finally, we apply the resulting filters to a frequency-flat multi-input multi-output (MIMO) transmission channel. Although the matrix PC algorithm is computationally cheaper than the matrix CS algorithm, we have shown through analysis that the two methods are equal if we assume i.i.d. transmit symbols and uncorrelated white Gaussian noise. Simulation results have shown that the matrix multi-stage WF (MSWF), which approximates the WF in a Krylov subspace, is partially outperformed in the considered MIMO case.

Original languageEnglish
Title of host publicationProceedings of the 3rd IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2003
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages82-85
Number of pages4
ISBN (Electronic)0780382927, 9780780382923
DOIs
StatePublished - 2003
Event3rd IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2003 - Darmstadt, Germany
Duration: 14 Dec 200317 Dec 2003

Publication series

NameProceedings of the 3rd IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2003

Conference

Conference3rd IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2003
Country/TerritoryGermany
CityDarmstadt
Period14/12/0317/12/03

Keywords

  • Covariance matrix
  • Equations
  • Frequency
  • Gaussian noise
  • Interference
  • MIMO
  • Mean square error methods
  • Robustness
  • Signal processing algorithms
  • Wiener filter

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