IEICE TRANSACTIONS on Fundamentals
A Subspace Blind Identification Algorithm with Reduced Computational Complexity--Colored Noise Case--
Summary :
We have proposed in [5] a practical blind channel identification algorithm for the white observation noise. In this paper, we examine the effectiveness of the algorithm given in [5] for the colored observation noise. The proposed algorithm utilizes Gram-Schmidt orthogonalization procedure and estimates (1) the channel order, (2) the noise variance and then (3) the channel impulse response with less computational complexity compared to the conventional algorithms using eigenvalue decomposition. It can be shown through numerical examples that the algorithm proposed in [5] is quite effective in the colored noise case.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E88-A No.7 pp.2015-2018
- Publication Date
- 2005/07/01
- Publicized
- Online ISSN
- DOI
- 10.1093/ietfec/e88-a.7.2015
- Type of Manuscript
- LETTER
- Category
- Digital Signal Processing
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Nari TANABE, Toshihiro FURUKAWA, Kohichi SAKANIWA, Shigeo TSUJII, "A Subspace Blind Identification Algorithm with Reduced Computational Complexity--Colored Noise Case--" in IEICE TRANSACTIONS on Fundamentals,
vol. E88-A, no. 7, pp. 2015-2018, July 2005, doi: 10.1093/ietfec/e88-a.7.2015.
Abstract: We have proposed in [5] a practical blind channel identification algorithm for the white observation noise. In this paper, we examine the effectiveness of the algorithm given in [5] for the colored observation noise. The proposed algorithm utilizes Gram-Schmidt orthogonalization procedure and estimates (1) the channel order, (2) the noise variance and then (3) the channel impulse response with less computational complexity compared to the conventional algorithms using eigenvalue decomposition. It can be shown through numerical examples that the algorithm proposed in [5] is quite effective in the colored noise case.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e88-a.7.2015/_p
Copy
@ARTICLE{e88-a_7_2015,
author={Nari TANABE, Toshihiro FURUKAWA, Kohichi SAKANIWA, Shigeo TSUJII, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Subspace Blind Identification Algorithm with Reduced Computational Complexity--Colored Noise Case--},
year={2005},
volume={E88-A},
number={7},
pages={2015-2018},
abstract={We have proposed in [5] a practical blind channel identification algorithm for the white observation noise. In this paper, we examine the effectiveness of the algorithm given in [5] for the colored observation noise. The proposed algorithm utilizes Gram-Schmidt orthogonalization procedure and estimates (1) the channel order, (2) the noise variance and then (3) the channel impulse response with less computational complexity compared to the conventional algorithms using eigenvalue decomposition. It can be shown through numerical examples that the algorithm proposed in [5] is quite effective in the colored noise case.},
keywords={},
doi={10.1093/ietfec/e88-a.7.2015},
ISSN={},
month={July},}
Copy
TY - JOUR
TI - A Subspace Blind Identification Algorithm with Reduced Computational Complexity--Colored Noise Case--
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2015
EP - 2018
AU - Nari TANABE
AU - Toshihiro FURUKAWA
AU - Kohichi SAKANIWA
AU - Shigeo TSUJII
PY - 2005
DO - 10.1093/ietfec/e88-a.7.2015
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E88-A
IS - 7
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - July 2005
AB - We have proposed in [5] a practical blind channel identification algorithm for the white observation noise. In this paper, we examine the effectiveness of the algorithm given in [5] for the colored observation noise. The proposed algorithm utilizes Gram-Schmidt orthogonalization procedure and estimates (1) the channel order, (2) the noise variance and then (3) the channel impulse response with less computational complexity compared to the conventional algorithms using eigenvalue decomposition. It can be shown through numerical examples that the algorithm proposed in [5] is quite effective in the colored noise case.
ER -
English
