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
Isyarat mengalami herotan tak linear, linear dan aditif apabila dihantar melalui saluran. Penyamaan linear biasanya digunakan dalam penerima untuk mengimbangi herotan saluran linear. Sebagai alternatif, struktur penyamaan baru yang menggunakan pengiraan saraf telah dibangunkan untuk mengimbangi herotan saluran tak linear. Dalam makalah ini, kami mencadangkan pengesan saraf berdasarkan peta penyusunan diri (SOM) dalam sistem 16 QAM. Skim yang dicadangkan menggunakan algoritma SOM dan pengesan simbol demi simbol untuk membentuk pengesan saraf, dan ia menyesuaikan dengan baik kepada keadaan saluran yang berubah-ubah, termasuk herotan tak linear kerana sifat pemeliharaan topologi algoritma SOM. Menurut analisis teori dan keputusan simulasi komputer, skema yang dicadangkan ditunjukkan mempunyai prestasi yang lebih baik daripada penyamaan linear tradisional apabila berhadapan dengan herotan tak linear.
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Salinan
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
Salinan
@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},}
Salinan
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 -