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
비선형적으로 왜곡된 신호를 검출하는 것은 통신에서 필수적인 문제입니다. 최근에는 특히 비선형 왜곡을 보상하는 성능을 향상시키기 위해 기존의 등화기를 결합한 신경망이 사용되었습니다. 본 논문에서는 기존의 기호별 검출기와 결합된 SOM(Self-Organizing Map)을 결정 피드백 등화기(DFE)의 출력 후 적응형 검출기로 사용합니다. 왜곡. 제안된 방식에서는 상자 거리를 사용하여 SOM 알고리즘의 승리 뉴런의 이웃을 정의합니다. 비선형 왜곡이 있는 16 QAM 및 64 QAM 시스템 모두에서 오류 성능이 조사되었습니다. 시뮬레이션 결과는 기존 DFE 방식에 비해 SOM 검출기를 사용하면 시스템 성능이 크게 향상되는 것을 보여줍니다.
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부
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
부
@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},}
부
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 -