Blind Channel Identification Based on Eigenvalue Decomposition Using Constrained LMS Algorithm
Summary :
Blind adaptive channel identification of communication channels is a problem of important current theoretical and practical concerns. Recently proposed solutions for this problem exploit the diversity induced by antenna array or time oversampling, leading to the so-called, second order statistics techniques. Adaptive blind channel identification techniques based on a off-line least-squares approach have been proposed but this method assumes noise-free case. The method resorts to an adaptive filter with a linear constraint. This paper proposes a new approach based on eigenvalue decomposition. Indeed, the eigenvector corresponding to the minimum eigenvalue of the covariance matrix of the received signals contains the channel impulse response. And we present a adaptive algorithm to solve this problem. The performance of the proposed technique is evaluated over real measured channel and is compared to existing algorithms.
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- IEICE TRANSACTIONS on Communications Vol.E85-B No.5 pp.961-966
- Publication Date
- 2002/05/01
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- Special Section LETTER (Wireless Communications Issue)
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Kyung Seung AHN, Eul Chool BYUN, Heung Ki BAIK, "Blind Channel Identification Based on Eigenvalue Decomposition Using Constrained LMS Algorithm" in IEICE TRANSACTIONS on Communications,
vol. E85-B, no. 5, pp. 961-966, May 2002, doi: .
Abstract: Blind adaptive channel identification of communication channels is a problem of important current theoretical and practical concerns. Recently proposed solutions for this problem exploit the diversity induced by antenna array or time oversampling, leading to the so-called, second order statistics techniques. Adaptive blind channel identification techniques based on a off-line least-squares approach have been proposed but this method assumes noise-free case. The method resorts to an adaptive filter with a linear constraint. This paper proposes a new approach based on eigenvalue decomposition. Indeed, the eigenvector corresponding to the minimum eigenvalue of the covariance matrix of the received signals contains the channel impulse response. And we present a adaptive algorithm to solve this problem. The performance of the proposed technique is evaluated over real measured channel and is compared to existing algorithms.
URL: https://global.ieice.org/en_transactions/communications/10.1587/e85-b_5_961/_p
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@ARTICLE{e85-b_5_961,
author={Kyung Seung AHN, Eul Chool BYUN, Heung Ki BAIK, },
journal={IEICE TRANSACTIONS on Communications},
title={Blind Channel Identification Based on Eigenvalue Decomposition Using Constrained LMS Algorithm},
year={2002},
volume={E85-B},
number={5},
pages={961-966},
abstract={Blind adaptive channel identification of communication channels is a problem of important current theoretical and practical concerns. Recently proposed solutions for this problem exploit the diversity induced by antenna array or time oversampling, leading to the so-called, second order statistics techniques. Adaptive blind channel identification techniques based on a off-line least-squares approach have been proposed but this method assumes noise-free case. The method resorts to an adaptive filter with a linear constraint. This paper proposes a new approach based on eigenvalue decomposition. Indeed, the eigenvector corresponding to the minimum eigenvalue of the covariance matrix of the received signals contains the channel impulse response. And we present a adaptive algorithm to solve this problem. The performance of the proposed technique is evaluated over real measured channel and is compared to existing algorithms.},
keywords={},
doi={},
ISSN={},
month={May},}
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TY - JOUR
TI - Blind Channel Identification Based on Eigenvalue Decomposition Using Constrained LMS Algorithm
T2 - IEICE TRANSACTIONS on Communications
SP - 961
EP - 966
AU - Kyung Seung AHN
AU - Eul Chool BYUN
AU - Heung Ki BAIK
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Communications
SN -
VL - E85-B
IS - 5
JA - IEICE TRANSACTIONS on Communications
Y1 - May 2002
AB - Blind adaptive channel identification of communication channels is a problem of important current theoretical and practical concerns. Recently proposed solutions for this problem exploit the diversity induced by antenna array or time oversampling, leading to the so-called, second order statistics techniques. Adaptive blind channel identification techniques based on a off-line least-squares approach have been proposed but this method assumes noise-free case. The method resorts to an adaptive filter with a linear constraint. This paper proposes a new approach based on eigenvalue decomposition. Indeed, the eigenvector corresponding to the minimum eigenvalue of the covariance matrix of the received signals contains the channel impulse response. And we present a adaptive algorithm to solve this problem. The performance of the proposed technique is evaluated over real measured channel and is compared to existing algorithms.
ER -
English
