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
Um sinal sofre distorção não linear, linear e aditiva quando transmitido através de um canal. Equalizadores lineares são comumente usados em receptores para compensar a distorção linear do canal. Como alternativa, novas estruturas de equalizador utilizando computação neural foram desenvolvidas para compensar a distorção não linear do canal. Neste artigo, propomos um detector neural baseado em mapa auto-organizado (SOM) em um sistema 16 QAM. O esquema proposto usa o algoritmo SOM e o detector símbolo por símbolo para formar um detector neural, e se adapta bem às mudanças nas condições do canal, incluindo distorções não lineares devido à propriedade de preservação da topologia do algoritmo SOM. De acordo com a análise teórica e os resultados da simulação computacional, o esquema proposto apresenta melhor desempenho que o equalizador linear tradicional quando enfrenta distorção não linear.
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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},}
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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 -