The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Un signal souffre d’une distorsion non linéaire, linéaire et additive lorsqu’il est transmis via un canal. Les égaliseurs linéaires sont couramment utilisés dans les récepteurs pour compenser la distorsion linéaire du canal. Comme alternative, de nouvelles structures d'égaliseur utilisant le calcul neuronal ont été développées pour compenser la distorsion non linéaire du canal. Dans cet article, nous proposons un détecteur neuronal basé sur une carte auto-organisatrice (SOM) dans un système 16 QAM. Le schéma proposé utilise l'algorithme SOM et le détecteur symbole par symbole pour former un détecteur neuronal. Il s'adapte bien aux conditions changeantes du canal, y compris aux distorsions non linéaires, en raison de la propriété de préservation de la topologie de l'algorithme SOM. Selon l'analyse théorique et les résultats de la simulation informatique, le schéma proposé s'avère avoir de meilleures performances que l'égaliseur linéaire traditionnel face à une distorsion non linéaire.
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Hua LIN, Xiaoqiu WANG, Jianming LU, Takashi YAHAGI, "Analysis of a Neural Detector Based on Self-Organizing Map in a 16 QAM System" in IEICE TRANSACTIONS on Communications,
vol. E84-B, no. 9, pp. 2628-2634, September 2001, doi: .
Abstract: A signal suffers from nonlinear, linear, and additive distortion when transmitted through a channel. Linear equalizers are commonly used in receivers to compensate for linear channel distortion. As an alternative, novel equalizer structures utilizing neural computation have been developed for compensating for nonlinear channel distortion. In this paper, we propose a neural detector based on self-organizing map (SOM) in a 16 QAM system. The proposed scheme uses the SOM algorithm and symbol-by-symbol detector to form a neural detector, and it adapts well to the changing channel conditions, including nonlinear distortions because of the topology-preserving property of the SOM algorithm. According to the theoretical analysis and computer simulation results, the proposed scheme is shown to have better performance than traditional linear equalizer when facing with nonlinear distortion.
URL: https://global.ieice.org/en_transactions/communications/10.1587/e84-b_9_2628/_p
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@ARTICLE{e84-b_9_2628,
author={Hua LIN, Xiaoqiu WANG, Jianming LU, Takashi YAHAGI, },
journal={IEICE TRANSACTIONS on Communications},
title={Analysis of a Neural Detector Based on Self-Organizing Map in a 16 QAM System},
year={2001},
volume={E84-B},
number={9},
pages={2628-2634},
abstract={A signal suffers from nonlinear, linear, and additive distortion when transmitted through a channel. Linear equalizers are commonly used in receivers to compensate for linear channel distortion. As an alternative, novel equalizer structures utilizing neural computation have been developed for compensating for nonlinear channel distortion. In this paper, we propose a neural detector based on self-organizing map (SOM) in a 16 QAM system. The proposed scheme uses the SOM algorithm and symbol-by-symbol detector to form a neural detector, and it adapts well to the changing channel conditions, including nonlinear distortions because of the topology-preserving property of the SOM algorithm. According to the theoretical analysis and computer simulation results, the proposed scheme is shown to have better performance than traditional linear equalizer when facing with nonlinear distortion.},
keywords={},
doi={},
ISSN={},
month={September},}
Copier
TY - JOUR
TI - Analysis of a Neural Detector Based on Self-Organizing Map in a 16 QAM System
T2 - IEICE TRANSACTIONS on Communications
SP - 2628
EP - 2634
AU - Hua LIN
AU - Xiaoqiu WANG
AU - Jianming LU
AU - Takashi YAHAGI
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Communications
SN -
VL - E84-B
IS - 9
JA - IEICE TRANSACTIONS on Communications
Y1 - September 2001
AB - A signal suffers from nonlinear, linear, and additive distortion when transmitted through a channel. Linear equalizers are commonly used in receivers to compensate for linear channel distortion. As an alternative, novel equalizer structures utilizing neural computation have been developed for compensating for nonlinear channel distortion. In this paper, we propose a neural detector based on self-organizing map (SOM) in a 16 QAM system. The proposed scheme uses the SOM algorithm and symbol-by-symbol detector to form a neural detector, and it adapts well to the changing channel conditions, including nonlinear distortions because of the topology-preserving property of the SOM algorithm. According to the theoretical analysis and computer simulation results, the proposed scheme is shown to have better performance than traditional linear equalizer when facing with nonlinear distortion.
ER -