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
La détection de signaux déformés de manière non linéaire est un problème essentiel dans les télécommunications. Récemment, un égaliseur conventionnel combiné à un réseau neuronal a été utilisé pour améliorer les performances, notamment en compensant les distorsions non linéaires. Dans cet article, la carte auto-organisatrice (SOM) combinée au détecteur symbole par symbole conventionnel est utilisée comme détecteur adaptatif après la sortie de l'égaliseur à retour de décision (DFE), qui met à jour les niveaux de décision pour suivre l'évolution non linéaire. distorsions. Dans le schéma proposé, nous utilisons la distance box pour définir le voisinage du neurone gagnant de l’algorithme SOM. Les performances d'erreur ont été étudiées dans les systèmes QAM 16 et QAM 64 avec distorsions non linéaires. Les résultats de simulation ont montré que les performances du système sont remarquablement améliorées en utilisant le détecteur SOM par rapport au schéma DFE conventionnel.
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Xiaoqiu WANG, Hua LIN, Jianming LU, Takashi YAHAGI, "Detection of Nonlinearly Distorted M-ary QAM Signals Using Self-Organizing Map" in IEICE TRANSACTIONS on Fundamentals,
vol. E84-A, no. 8, pp. 1969-1976, August 2001, doi: .
Abstract: Detection of nonlinearly distorted signals is an essential problem in telecommunications. Recently, neural network combined conventional equalizer has been used to improve the performance especially in compensating for nonlinear distortions. In this paper, the self-organizing map (SOM) combined with the conventional symbol-by-symbol detector is used as an adaptive detector after the output of the decision feedback equalizer (DFE), which updates the decision levels to follow up the nonlinear distortions. In the proposed scheme, we use the box distance to define the neighborhood of the winning neuron of the SOM algorithm. The error performance has been investigated in both 16 QAM and 64 QAM systems with nonlinear distortions. Simulation results have shown that the system performance is remarkably improved by using SOM detector compared with the conventional DFE scheme.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e84-a_8_1969/_p
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@ARTICLE{e84-a_8_1969,
author={Xiaoqiu WANG, Hua LIN, Jianming LU, Takashi YAHAGI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Detection of Nonlinearly Distorted M-ary QAM Signals Using Self-Organizing Map},
year={2001},
volume={E84-A},
number={8},
pages={1969-1976},
abstract={Detection of nonlinearly distorted signals is an essential problem in telecommunications. Recently, neural network combined conventional equalizer has been used to improve the performance especially in compensating for nonlinear distortions. In this paper, the self-organizing map (SOM) combined with the conventional symbol-by-symbol detector is used as an adaptive detector after the output of the decision feedback equalizer (DFE), which updates the decision levels to follow up the nonlinear distortions. In the proposed scheme, we use the box distance to define the neighborhood of the winning neuron of the SOM algorithm. The error performance has been investigated in both 16 QAM and 64 QAM systems with nonlinear distortions. Simulation results have shown that the system performance is remarkably improved by using SOM detector compared with the conventional DFE scheme.},
keywords={},
doi={},
ISSN={},
month={August},}
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TY - JOUR
TI - Detection of Nonlinearly Distorted M-ary QAM Signals Using Self-Organizing Map
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1969
EP - 1976
AU - Xiaoqiu WANG
AU - Hua LIN
AU - Jianming LU
AU - Takashi YAHAGI
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E84-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 2001
AB - Detection of nonlinearly distorted signals is an essential problem in telecommunications. Recently, neural network combined conventional equalizer has been used to improve the performance especially in compensating for nonlinear distortions. In this paper, the self-organizing map (SOM) combined with the conventional symbol-by-symbol detector is used as an adaptive detector after the output of the decision feedback equalizer (DFE), which updates the decision levels to follow up the nonlinear distortions. In the proposed scheme, we use the box distance to define the neighborhood of the winning neuron of the SOM algorithm. The error performance has been investigated in both 16 QAM and 64 QAM systems with nonlinear distortions. Simulation results have shown that the system performance is remarkably improved by using SOM detector compared with the conventional DFE scheme.
ER -