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Estimating MIMO channel covariances from training data under the Kronecker model
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-6855-5868
2009 (English)In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 89, no 1, 1-13 p.Article in journal (Refereed) Published
Abstract [en]

Many algorithms for transmission in multiple input multiple output (MIMO) communication systems rely on second order statistics of the channel realizations. The problem of estimating such second order statistics of MIMO channels, based on limited amounts of training data, is treated in this article. It is assumed that the Kronecker model holds. This implies that the channel covariance is the Kronecker product of one covariance matrix that is associated with the array and the scattering at the transmitter and one that is associated with the receive array and the scattering at the receiver. The proposed estimator uses training data from a number of signal blocks (received during independent fades of the MIMO channel) to compute the estimate. This is in contrast to methods that assume that the channel realizations are directly available, or possible to estimate almost without error. It is also demonstrated how methods that make use of the training data indirectly via channel estimates can be biased. An estimator is derived that can, in an asymptotically optimal way, use, not only the structure implied by the Kronecker assumption, but also linear structure on the transmit- and receive covariance matrices. The performance of the proposed estimator is analyzed and numerical simulations illustrate the results and also provide insight into the small sample behaviour of the proposed method.

Place, publisher, year, edition, pages
2009. Vol. 89, no 1, 1-13 p.
Keyword [en]
MIMO channel modelling, Parameter estimation, Performance analysis, Covariance matching, Covariance matrix estimation, propagation channels, wireless systems, parameter, capacity, matrices
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-17947DOI: 10.1016/j.sigpro.2008.06.014ISI: 000260699400001Scopus ID: 2-s2.0-52049110499OAI: oai:DiVA.org:kth-17947DiVA: diva2:335992
Note
QC 20100525Available from: 2010-08-05 Created: 2010-08-05 Last updated: 2017-12-12Bibliographically approved

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