Adaptive blind MIMO system identification using Principal Component neural models

Konstantinos I. Diamantaras; Athina P. Petropulu

Adaptive blind MIMO system identification using Principal Component neural models

Konstantinos I. Diamantaras, Athina P. Petropulu

Research output: Contribution to conference › Paper › peer-review

Abstract

We treat the blind identification problem for a n×n MIMO system using second order frequency-domain statistics and Asymmetric PCA neural models. It is assumed that the source signals are colored, stationary, and pair-wise independent sequences with otherwise unknown statistics. We introduce a set of invariant indices that are used to tackle the problem of frequency-dependent ambiguities in the ordering and the phase of the retrieved singular vectors. For the case of 2×2 systems we present a complete identification procedure based on the corresponding invariant indices and we conjecture that these indices will be instrumental in the solution of the general n×n problem as well.

Original language	English (US)
Pages	980-984
Number of pages	5
State	Published - 1999
Externally published	Yes
Event	International Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA Duration: Jul 10 1999 → Jul 16 1999

Other

Other	International Joint Conference on Neural Networks (IJCNN'99)
City	Washington, DC, USA
Period	7/10/99 → 7/16/99

All Science Journal Classification (ASJC) codes

Software
Artificial Intelligence

Cite this

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title = "Adaptive blind MIMO system identification using Principal Component neural models",

abstract = "We treat the blind identification problem for a n×n MIMO system using second order frequency-domain statistics and Asymmetric PCA neural models. It is assumed that the source signals are colored, stationary, and pair-wise independent sequences with otherwise unknown statistics. We introduce a set of invariant indices that are used to tackle the problem of frequency-dependent ambiguities in the ordering and the phase of the retrieved singular vectors. For the case of 2×2 systems we present a complete identification procedure based on the corresponding invariant indices and we conjecture that these indices will be instrumental in the solution of the general n×n problem as well.",

author = "Diamantaras, {Konstantinos I.} and Petropulu, {Athina P.}",

year = "1999",

language = "English (US)",

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note = "International Joint Conference on Neural Networks (IJCNN'99) ; Conference date: 10-07-1999 Through 16-07-1999",

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AU - Diamantaras, Konstantinos I.

AU - Petropulu, Athina P.

PY - 1999

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AB - We treat the blind identification problem for a n×n MIMO system using second order frequency-domain statistics and Asymmetric PCA neural models. It is assumed that the source signals are colored, stationary, and pair-wise independent sequences with otherwise unknown statistics. We introduce a set of invariant indices that are used to tackle the problem of frequency-dependent ambiguities in the ordering and the phase of the retrieved singular vectors. For the case of 2×2 systems we present a complete identification procedure based on the corresponding invariant indices and we conjecture that these indices will be instrumental in the solution of the general n×n problem as well.

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M3 - Paper

AN - SCOPUS:0033308457

SP - 980

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T2 - International Joint Conference on Neural Networks (IJCNN'99)

Y2 - 10 July 1999 through 16 July 1999

ER -

Adaptive blind MIMO system identification using Principal Component neural models

Abstract

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All Science Journal Classification (ASJC) codes

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