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Further Results on Optimally Weighted Subspace Based Blind Channel Estimation
KTH, School of Electrical Engineering (EES), Signal Processing.
KTH, Superseded Departments, Signals, Sensors and Systems.ORCID iD: 0000-0003-2298-6774
1998 (English)In: Proceedings of the 32th Asilomar Conference on Signals, Systems and Computers, IEEE , 1998, 1579-1583 p.Conference paper (Refereed)
Abstract [en]

Subspace based identification of model parameters requires a low rank data model. To obtain such a model it is common to collect several snapshots of the observed vector valued sequence in a larger vector. In many cases, this “window” procedure results in a new vector valued process which can be viewed as originating from a low rank data model. When studying the performance of weighted subspace based techniques this window procedure complicates both the analysis and the implementation. In this paper an attempt is made to clarify the theoretical and numerical difficulties when applying weighting in such scenarios

Place, publisher, year, edition, pages
IEEE , 1998. 1579-1583 p.
Keyword [en]
Additive noise, Blind equalizers, Covariance matrix, Eigenvalues and eigenfunctions, Gaussian noise, Parameter estimation, Symmetric matrices, Vectors
National Category
URN: urn:nbn:se:kth:diva-77438DOI: 10.1109/ACSSC.1998.751592OAI: diva2:491612
Thirty-Second Asilomar Conference on Signals, Systems and Computers
Invited paper NR 20140805Available from: 2012-02-06 Created: 2012-02-06 Last updated: 2012-02-06Bibliographically approved

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