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
Etant donné que le canal de communication mobile change avec le temps, il est nécessaire d'utiliser des égaliseurs de canal adaptatifs afin de lutter contre les effets de distorsion du canal. L'algorithme des moindres carrés moyens (LMS) est l'un des algorithmes d'égalisation de canal les plus populaires et est préféré à d'autres algorithmes tels que les moindres carrés récursifs (RLS) et l'estimation de séquence de vraisemblance maximale (MLSE) lorsque la simplicité est le facteur de décision dominant. Cependant, l’algorithme LMS souffre de performances et d’une vitesse de convergence médiocres au cours de la période de formation spécifiée par la plupart des normes. Le but de cette étude est d'améliorer la vitesse de convergence et les performances de l'algorithme LMS en ajustant la taille du pas à l'aide de la logique floue. La méthode proposée est comparée à l'égaliseur de rétroaction de décision à filtre adapté au canal (CMF-DFE) [1] qui fournit une diversité de propagation multi-trajets en collectant l'énergie dans le canal, à l'égaliseur de rétroaction de décision à erreur quadratique moyenne minimale (MMSE-DFE) [ 2] qui est l'un des égaliseurs les plus performants pour la transmission de paquets de données, LMS-DFE normalisé (N-LMS-DFE) [3] , LMS-DFE à taille de pas variable (VSS) [4] , LMS-DFE flou [5 ],[6] et RLS-DFE [7] . Les résultats de simulation obtenus à l'aide des normes HIPERLAN/1 ont démontré que l'algorithme LMS-DFE proposé basé sur la logique floue présente des performances considérablement meilleures que les autres.
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Ali OZEN, Ismail KAYA, Birol SOYSAL, "Design of a Fuzzy Based Outer Loop Controller for Improving the Training Performance of LMS Algorithm" in IEICE TRANSACTIONS on Fundamentals,
vol. E91-A, no. 12, pp. 3738-3744, December 2008, doi: 10.1093/ietfec/e91-a.12.3738.
Abstract: Because of the fact that mobile communication channel changes by time, it is necessary to employ adaptive channel equalizers in order to combat the distorting effects of the channel. Least Mean Squares (LMS) algorithm is one of the most popular channel equalization algorithms and is preferred over other algorithms such as the Recursive Least Squares (RLS) and Maximum Likelihood Sequence Estimation (MLSE) when simplicity is the dominant decision factor. However, LMS algorithm suffers from poor performance and convergence speed within the training period specified by most of the standards. The aim of this study is to improve the convergence speed and performance of the LMS algorithm by adjusting the step size using fuzzy logic. The proposed method is compared with the Channel Matched Filter-Decision Feedback Equalizer (CMF-DFE) [1] which provides multi path propagation diversity by collecting the energy in the channel, Minimum Mean Square Error-Decision Feedback Equalizer (MMSE-DFE) [2] which is one of the most successful equalizers for the data packet transmission, normalized LMS-DFE (N-LMS-DFE) [3] , variable step size (VSS) LMS-DFE [4] , fuzzy LMS-DFE [5],[6] and RLS-DFE [7] . The obtained simulation results using HIPERLAN/1 standards have demonstrated that the proposed LMS-DFE algorithm based on fuzzy logic has considerably better performance than others.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e91-a.12.3738/_p
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@ARTICLE{e91-a_12_3738,
author={Ali OZEN, Ismail KAYA, Birol SOYSAL, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Design of a Fuzzy Based Outer Loop Controller for Improving the Training Performance of LMS Algorithm},
year={2008},
volume={E91-A},
number={12},
pages={3738-3744},
abstract={Because of the fact that mobile communication channel changes by time, it is necessary to employ adaptive channel equalizers in order to combat the distorting effects of the channel. Least Mean Squares (LMS) algorithm is one of the most popular channel equalization algorithms and is preferred over other algorithms such as the Recursive Least Squares (RLS) and Maximum Likelihood Sequence Estimation (MLSE) when simplicity is the dominant decision factor. However, LMS algorithm suffers from poor performance and convergence speed within the training period specified by most of the standards. The aim of this study is to improve the convergence speed and performance of the LMS algorithm by adjusting the step size using fuzzy logic. The proposed method is compared with the Channel Matched Filter-Decision Feedback Equalizer (CMF-DFE) [1] which provides multi path propagation diversity by collecting the energy in the channel, Minimum Mean Square Error-Decision Feedback Equalizer (MMSE-DFE) [2] which is one of the most successful equalizers for the data packet transmission, normalized LMS-DFE (N-LMS-DFE) [3] , variable step size (VSS) LMS-DFE [4] , fuzzy LMS-DFE [5],[6] and RLS-DFE [7] . The obtained simulation results using HIPERLAN/1 standards have demonstrated that the proposed LMS-DFE algorithm based on fuzzy logic has considerably better performance than others.},
keywords={},
doi={10.1093/ietfec/e91-a.12.3738},
ISSN={1745-1337},
month={December},}
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TY - JOUR
TI - Design of a Fuzzy Based Outer Loop Controller for Improving the Training Performance of LMS Algorithm
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 3738
EP - 3744
AU - Ali OZEN
AU - Ismail KAYA
AU - Birol SOYSAL
PY - 2008
DO - 10.1093/ietfec/e91-a.12.3738
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E91-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2008
AB - Because of the fact that mobile communication channel changes by time, it is necessary to employ adaptive channel equalizers in order to combat the distorting effects of the channel. Least Mean Squares (LMS) algorithm is one of the most popular channel equalization algorithms and is preferred over other algorithms such as the Recursive Least Squares (RLS) and Maximum Likelihood Sequence Estimation (MLSE) when simplicity is the dominant decision factor. However, LMS algorithm suffers from poor performance and convergence speed within the training period specified by most of the standards. The aim of this study is to improve the convergence speed and performance of the LMS algorithm by adjusting the step size using fuzzy logic. The proposed method is compared with the Channel Matched Filter-Decision Feedback Equalizer (CMF-DFE) [1] which provides multi path propagation diversity by collecting the energy in the channel, Minimum Mean Square Error-Decision Feedback Equalizer (MMSE-DFE) [2] which is one of the most successful equalizers for the data packet transmission, normalized LMS-DFE (N-LMS-DFE) [3] , variable step size (VSS) LMS-DFE [4] , fuzzy LMS-DFE [5],[6] and RLS-DFE [7] . The obtained simulation results using HIPERLAN/1 standards have demonstrated that the proposed LMS-DFE algorithm based on fuzzy logic has considerably better performance than others.
ER -