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
Recentemente, as redes neurais (NNs) têm sido extensivamente aplicadas a muitos problemas de processamento de sinais devido à sua capacidade robusta de formar regiões de decisão complexas. Em particular, as redes neurais acrescentam flexibilidade ao projeto de equalizadores para sistemas de comunicação digital. A rede neural recorrente (RNN) é um tipo de rede neural com um ou mais loops de feedback, enquanto o mapa auto-organizado (SOM) é caracterizado pela formação de um mapa topográfico dos padrões de entrada nos quais as localizações espaciais (ou seja, coordenadas) dos neurônios na rede são indicativos de características estatísticas intrínsecas contidas nos padrões de entrada. Neste artigo, propomos uma nova estrutura de receptor combinando um equalizador RNN adaptativo com um detector SOM sob ISI grave e distorção não linear no sistema QAM. De acordo com a análise teórica e os resultados da simulação computacional, o desempenho do esquema proposto mostra-se bastante eficaz na equalização de canais sob distorção não linear.
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Xiaoqiu WANG, Hua LIN, Jianming LU, Takashi YAHAGI, "Combining Recurrent Neural Networks with Self-Organizing Map for Channel Equalization" in IEICE TRANSACTIONS on Communications,
vol. E85-B, no. 10, pp. 2227-2235, October 2002, doi: .
Abstract: Recently, neural networks (NNs) have been extensively applied to many signal processing problem due to their robust abilities to form complex decision regions. In particular, neural networks add flexibility to the design of equalizers for digital communication systems. Recurrent neural network (RNN) is a kind of neural network with one or more feedback loops, whereas self-organizing map (SOM) is characterized by the formation of a topographic map of the input patterns in which the spatial locations (i.e., coordinates) of the neurons in the lattice are indicative of intrinsic statistical features contained in the input patterns. In this paper, we propose a novel receiver structure by combining adaptive RNN equalizer with a SOM detector under serious ISI and nonlinear distortion in QAM system. According to the theoretical analysis and computer simulation results, the performance of the proposed scheme is shown to be quite effective in channel equalization under nonlinear distortion.
URL: https://global.ieice.org/en_transactions/communications/10.1587/e85-b_10_2227/_p
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@ARTICLE{e85-b_10_2227,
author={Xiaoqiu WANG, Hua LIN, Jianming LU, Takashi YAHAGI, },
journal={IEICE TRANSACTIONS on Communications},
title={Combining Recurrent Neural Networks with Self-Organizing Map for Channel Equalization},
year={2002},
volume={E85-B},
number={10},
pages={2227-2235},
abstract={Recently, neural networks (NNs) have been extensively applied to many signal processing problem due to their robust abilities to form complex decision regions. In particular, neural networks add flexibility to the design of equalizers for digital communication systems. Recurrent neural network (RNN) is a kind of neural network with one or more feedback loops, whereas self-organizing map (SOM) is characterized by the formation of a topographic map of the input patterns in which the spatial locations (i.e., coordinates) of the neurons in the lattice are indicative of intrinsic statistical features contained in the input patterns. In this paper, we propose a novel receiver structure by combining adaptive RNN equalizer with a SOM detector under serious ISI and nonlinear distortion in QAM system. According to the theoretical analysis and computer simulation results, the performance of the proposed scheme is shown to be quite effective in channel equalization under nonlinear distortion.},
keywords={},
doi={},
ISSN={},
month={October},}
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TY - JOUR
TI - Combining Recurrent Neural Networks with Self-Organizing Map for Channel Equalization
T2 - IEICE TRANSACTIONS on Communications
SP - 2227
EP - 2235
AU - Xiaoqiu WANG
AU - Hua LIN
AU - Jianming LU
AU - Takashi YAHAGI
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Communications
SN -
VL - E85-B
IS - 10
JA - IEICE TRANSACTIONS on Communications
Y1 - October 2002
AB - Recently, neural networks (NNs) have been extensively applied to many signal processing problem due to their robust abilities to form complex decision regions. In particular, neural networks add flexibility to the design of equalizers for digital communication systems. Recurrent neural network (RNN) is a kind of neural network with one or more feedback loops, whereas self-organizing map (SOM) is characterized by the formation of a topographic map of the input patterns in which the spatial locations (i.e., coordinates) of the neurons in the lattice are indicative of intrinsic statistical features contained in the input patterns. In this paper, we propose a novel receiver structure by combining adaptive RNN equalizer with a SOM detector under serious ISI and nonlinear distortion in QAM system. According to the theoretical analysis and computer simulation results, the performance of the proposed scheme is shown to be quite effective in channel equalization under nonlinear distortion.
ER -