Blind identification of time-varying channels using second-order statistics

Michail K. Tsatsanis; Georgios B. Giannakis

doi:10.1109/ACSSC.1995.540533

Blind identification of time-varying channels using second-order statistics

Michail K. Tsatsanis, Georgios B. Giannakis

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Scopus citations

Abstract

Novel linear algorithms are proposed in this paper for estimating symbol spaced, time-varying FIR communication channels, without resorting to higher-order statistics. The proposed methods are applicable to channels where each time-varying tap coefficient can be described (with respect to time) as a linear combination of a finite number of basis functions. Examples of such channels include periodically varying ones or channels locally modeled by a truncated Taylor series or wavelet expansion. It is shown that the estimation of the basis expansion parameters is equivalent to estimating the parameters of an FIR single-input-many-outputs (SIMO) system. By exploiting this equivalence, a number of different blind subspace methods are applicable, which have been originally developed in the context of SIMO systems. Identifiability issues are investigated and some illustrative simulations are presented.

Original language	English (US)
Title of host publication	Conference Record of the 29th Asilomar Conference on Signals, Systems and Computers, ACSSC 1995
Editors	Avtar Singh
Publisher	IEEE Computer Society
Pages	162-166
Number of pages	5
ISBN (Electronic)	0818673702
DOIs	https://doi.org/10.1109/ACSSC.1995.540533
State	Published - 1995
Externally published	Yes
Event	29th Asilomar Conference on Signals, Systems and Computers, ACSSC 1995 - Pacific Grove, United States Duration: Oct 30 1995 → Nov 1 1995

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
Volume	1
ISSN (Print)	1058-6393

Conference

Conference	29th Asilomar Conference on Signals, Systems and Computers, ACSSC 1995
Country/Territory	United States
City	Pacific Grove
Period	10/30/95 → 11/1/95

Bibliographical note

Publisher Copyright:
© 1995 IEEE.

Publisher link

10.1109/ACSSC.1995.540533

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Tsatsanis, M. K., & Giannakis, G. B. (1995). Blind identification of time-varying channels using second-order statistics. In A. Singh (Ed.), Conference Record of the 29th Asilomar Conference on Signals, Systems and Computers, ACSSC 1995 (pp. 162-166). Article 540533 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 1). IEEE Computer Society. https://doi.org/10.1109/ACSSC.1995.540533

Blind identification of time-varying channels using second-order statistics. / Tsatsanis, Michail K.; Giannakis, Georgios B.
Conference Record of the 29th Asilomar Conference on Signals, Systems and Computers, ACSSC 1995. ed. / Avtar Singh. IEEE Computer Society, 1995. p. 162-166 540533 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Tsatsanis, MK & Giannakis, GB 1995, Blind identification of time-varying channels using second-order statistics. in A Singh (ed.), Conference Record of the 29th Asilomar Conference on Signals, Systems and Computers, ACSSC 1995., 540533, Conference Record - Asilomar Conference on Signals, Systems and Computers, vol. 1, IEEE Computer Society, pp. 162-166, 29th Asilomar Conference on Signals, Systems and Computers, ACSSC 1995, Pacific Grove, United States, 10/30/95. https://doi.org/10.1109/ACSSC.1995.540533

Tsatsanis MK, Giannakis GB. Blind identification of time-varying channels using second-order statistics. In Singh A, editor, Conference Record of the 29th Asilomar Conference on Signals, Systems and Computers, ACSSC 1995. IEEE Computer Society. 1995. p. 162-166. 540533. (Conference Record - Asilomar Conference on Signals, Systems and Computers). doi: 10.1109/ACSSC.1995.540533

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AB - Novel linear algorithms are proposed in this paper for estimating symbol spaced, time-varying FIR communication channels, without resorting to higher-order statistics. The proposed methods are applicable to channels where each time-varying tap coefficient can be described (with respect to time) as a linear combination of a finite number of basis functions. Examples of such channels include periodically varying ones or channels locally modeled by a truncated Taylor series or wavelet expansion. It is shown that the estimation of the basis expansion parameters is equivalent to estimating the parameters of an FIR single-input-many-outputs (SIMO) system. By exploiting this equivalence, a number of different blind subspace methods are applicable, which have been originally developed in the context of SIMO systems. Identifiability issues are investigated and some illustrative simulations are presented.

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Blind identification of time-varying channels using second-order statistics

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