IEICE TRANSACTIONS on Fundamentals
A Cluster Map Based Blind RBF Decision Feedback Equalizer with Reduced Computational Complexity
Summary :
Recently, a cluster map based blind RBF equalizer (CM-BRE) has been proposed. By utilizing the underlying structure characteristics of RBF equalizer, the CM-BRE can be implemented by the combination of neural-gas algorithm (NGA) with several sorting operations. Although the CM-BRE is able to achieve almost identical performance with the optimal RBF equalizer, the high computational load mainly caused by NGA limits it's application. In this paper, we propose a downsizing method that employs the inter-relation among RBF centers and significantly reduces the NGA's computational load. Furthermore, a method to determine the feedback vector is derived, then CM-BRE is extended to a cluster map based blind RBF decision feedback equalizer (CM-BRDFE). The proposed CM-BRDFE also shows the close performance with the optimal RBF decision feedback equalizer in simulations.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E87-A No.10 pp.2755-2760
- Publication Date
- 2004/10/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Digital Signal Processing
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Hai LIN, Katsumi YAMASHITA, "A Cluster Map Based Blind RBF Decision Feedback Equalizer with Reduced Computational Complexity" in IEICE TRANSACTIONS on Fundamentals,
vol. E87-A, no. 10, pp. 2755-2760, October 2004, doi: .
Abstract: Recently, a cluster map based blind RBF equalizer (CM-BRE) has been proposed. By utilizing the underlying structure characteristics of RBF equalizer, the CM-BRE can be implemented by the combination of neural-gas algorithm (NGA) with several sorting operations. Although the CM-BRE is able to achieve almost identical performance with the optimal RBF equalizer, the high computational load mainly caused by NGA limits it's application. In this paper, we propose a downsizing method that employs the inter-relation among RBF centers and significantly reduces the NGA's computational load. Furthermore, a method to determine the feedback vector is derived, then CM-BRE is extended to a cluster map based blind RBF decision feedback equalizer (CM-BRDFE). The proposed CM-BRDFE also shows the close performance with the optimal RBF decision feedback equalizer in simulations.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e87-a_10_2755/_p
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@ARTICLE{e87-a_10_2755,
author={Hai LIN, Katsumi YAMASHITA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Cluster Map Based Blind RBF Decision Feedback Equalizer with Reduced Computational Complexity},
year={2004},
volume={E87-A},
number={10},
pages={2755-2760},
abstract={Recently, a cluster map based blind RBF equalizer (CM-BRE) has been proposed. By utilizing the underlying structure characteristics of RBF equalizer, the CM-BRE can be implemented by the combination of neural-gas algorithm (NGA) with several sorting operations. Although the CM-BRE is able to achieve almost identical performance with the optimal RBF equalizer, the high computational load mainly caused by NGA limits it's application. In this paper, we propose a downsizing method that employs the inter-relation among RBF centers and significantly reduces the NGA's computational load. Furthermore, a method to determine the feedback vector is derived, then CM-BRE is extended to a cluster map based blind RBF decision feedback equalizer (CM-BRDFE). The proposed CM-BRDFE also shows the close performance with the optimal RBF decision feedback equalizer in simulations.},
keywords={},
doi={},
ISSN={},
month={October},}
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TY - JOUR
TI - A Cluster Map Based Blind RBF Decision Feedback Equalizer with Reduced Computational Complexity
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2755
EP - 2760
AU - Hai LIN
AU - Katsumi YAMASHITA
PY - 2004
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E87-A
IS - 10
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - October 2004
AB - Recently, a cluster map based blind RBF equalizer (CM-BRE) has been proposed. By utilizing the underlying structure characteristics of RBF equalizer, the CM-BRE can be implemented by the combination of neural-gas algorithm (NGA) with several sorting operations. Although the CM-BRE is able to achieve almost identical performance with the optimal RBF equalizer, the high computational load mainly caused by NGA limits it's application. In this paper, we propose a downsizing method that employs the inter-relation among RBF centers and significantly reduces the NGA's computational load. Furthermore, a method to determine the feedback vector is derived, then CM-BRE is extended to a cluster map based blind RBF decision feedback equalizer (CM-BRDFE). The proposed CM-BRDFE also shows the close performance with the optimal RBF decision feedback equalizer in simulations.
ER -
English
