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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Pengesanan isyarat herot tak linear merupakan masalah penting dalam telekomunikasi. Baru-baru ini, rangkaian neural gabungan penyamaan konvensional telah digunakan untuk meningkatkan prestasi terutamanya dalam mengimbangi herotan tak linear. Dalam kertas kerja ini, peta penyusunan diri (SOM) digabungkan dengan pengesan simbol demi simbol konvensional digunakan sebagai pengesan penyesuaian selepas keluaran penyamaan maklum balas keputusan (DFE), yang mengemas kini tahap keputusan untuk membuat susulan bukan linear. herotan. Dalam skema yang dicadangkan, kami menggunakan jarak kotak untuk menentukan kejiranan neuron pemenang algoritma SOM. Prestasi ralat telah disiasat dalam kedua-dua sistem 16 QAM dan 64 QAM dengan herotan tak linear. Keputusan simulasi telah menunjukkan bahawa prestasi sistem bertambah baik dengan menggunakan pengesan SOM berbanding dengan skema DFE konvensional.
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Salinan
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
Salinan
@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},}
Salinan
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 -