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
Dans cet article, nous proposons une méthode informatiquement efficace pour résoudre le problème d’attribution adaptative de sous-porteuses et d’allocation de bits (ASABA) de grande dimension d’un système de multiplexage par répartition orthogonale de la fréquence multi-utilisateurs. Notre algorithme se compose de trois étapes d'optimisation ordinale (OO) pour trouver une solution suffisamment bonne au problème considéré. Tout d'abord, nous reformulons le problème considéré pour le séparer en problème d'affectation de sous-porteuse et d'allocation de bits de telle sorte que la fonction objectif d'un modèle d'attribution de sous-porteuse réalisable soit l'allocation de bits optimale correspondante pour minimiser la puissance totale consommée. Ensuite, dans un premier temps, nous développons une fonction objective approximative pour évaluer les performances d'un modèle d'affectation de sous-porteuse et utilisons un algorithme génétique pour rechercher dans l'immense espace de solutions et sélectionner s meilleurs modèles d'attribution de sous-porteuses sur la base des valeurs objectives approximatives. Dans la deuxième étape, nous utilisons un réseau de neurones artificiels formés hors ligne pour estimer les valeurs objectives du s modèles d'affectation de sous-porteuses obtenus à l'étape 1 et sélectionnez le l meilleurs modèles. Dans la troisième étape, nous utilisons la fonction objectif exacte pour évaluer l Les modèles d'attribution de sous-porteuses obtenus à l'étape 2, et le meilleur associé à l'allocation de bits optimale correspondante est la solution suffisamment bonne que nous recherchons. Nous appliquons notre algorithme à de nombreux cas de problèmes ASABA de grande dimension et comparons les résultats avec ceux obtenus par quatre algorithmes existants. Les résultats des tests montrent que notre algorithme est le meilleur en termes de qualité de la solution et d’efficacité de calcul.
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Shin-Yeu LIN, Jung-Shou HUANG, "A Computationally Efficient Method for Large Dimension Subcarrier Assignment and Bit Allocation Problem of Multiuser OFDM System" in IEICE TRANSACTIONS on Communications,
vol. E91-B, no. 12, pp. 3966-3973, December 2008, doi: 10.1093/ietcom/e91-b.12.3966.
Abstract: In this paper, we propose a computationally efficient method to solve the large dimension Adaptive Subcarrier Assignment and Bit Allocation (ASABA) problem of multiuser orthogonal frequency division multiplexing system. Our algorithm consists of three Ordinal Optimization (OO) stages to find a good enough solution to the considered problem. First of all, we reformulate the considered problem to separate it into subcarrier assignment and bit allocation problem such that the objective function of a feasible subcarrier assignment pattern is the corresponding optimal bit allocation for minimizing the total consumed power. Then in the first stage, we develop an approximate objective function to evaluate the performance of a subcarrier assignment pattern and use a genetic algorithm to search through the huge solution space and select s best subcarrier assignment patterns based on the approximate objective values. In the second stage, we employ an off-line trained artificial neural network to estimate the objective values of the s subcarrier assignment patterns obtained in stage 1 and select the l best patterns. In the third stage, we use the exact objective function to evaluate the l subcarrier assignment patterns obtained in stage 2, and the best one associated with the corresponding optimal bit allocation is the good enough solution that we seek. We apply our algorithm to numerous cases of large-dimension ASABA problems and compare the results with those obtained by four existing algorithms. The test results show that our algorithm is the best in both aspects of solution quality and computational efficiency.
URL: https://global.ieice.org/en_transactions/communications/10.1093/ietcom/e91-b.12.3966/_p
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@ARTICLE{e91-b_12_3966,
author={Shin-Yeu LIN, Jung-Shou HUANG, },
journal={IEICE TRANSACTIONS on Communications},
title={A Computationally Efficient Method for Large Dimension Subcarrier Assignment and Bit Allocation Problem of Multiuser OFDM System},
year={2008},
volume={E91-B},
number={12},
pages={3966-3973},
abstract={In this paper, we propose a computationally efficient method to solve the large dimension Adaptive Subcarrier Assignment and Bit Allocation (ASABA) problem of multiuser orthogonal frequency division multiplexing system. Our algorithm consists of three Ordinal Optimization (OO) stages to find a good enough solution to the considered problem. First of all, we reformulate the considered problem to separate it into subcarrier assignment and bit allocation problem such that the objective function of a feasible subcarrier assignment pattern is the corresponding optimal bit allocation for minimizing the total consumed power. Then in the first stage, we develop an approximate objective function to evaluate the performance of a subcarrier assignment pattern and use a genetic algorithm to search through the huge solution space and select s best subcarrier assignment patterns based on the approximate objective values. In the second stage, we employ an off-line trained artificial neural network to estimate the objective values of the s subcarrier assignment patterns obtained in stage 1 and select the l best patterns. In the third stage, we use the exact objective function to evaluate the l subcarrier assignment patterns obtained in stage 2, and the best one associated with the corresponding optimal bit allocation is the good enough solution that we seek. We apply our algorithm to numerous cases of large-dimension ASABA problems and compare the results with those obtained by four existing algorithms. The test results show that our algorithm is the best in both aspects of solution quality and computational efficiency.},
keywords={},
doi={10.1093/ietcom/e91-b.12.3966},
ISSN={1745-1345},
month={December},}
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TY - JOUR
TI - A Computationally Efficient Method for Large Dimension Subcarrier Assignment and Bit Allocation Problem of Multiuser OFDM System
T2 - IEICE TRANSACTIONS on Communications
SP - 3966
EP - 3973
AU - Shin-Yeu LIN
AU - Jung-Shou HUANG
PY - 2008
DO - 10.1093/ietcom/e91-b.12.3966
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E91-B
IS - 12
JA - IEICE TRANSACTIONS on Communications
Y1 - December 2008
AB - In this paper, we propose a computationally efficient method to solve the large dimension Adaptive Subcarrier Assignment and Bit Allocation (ASABA) problem of multiuser orthogonal frequency division multiplexing system. Our algorithm consists of three Ordinal Optimization (OO) stages to find a good enough solution to the considered problem. First of all, we reformulate the considered problem to separate it into subcarrier assignment and bit allocation problem such that the objective function of a feasible subcarrier assignment pattern is the corresponding optimal bit allocation for minimizing the total consumed power. Then in the first stage, we develop an approximate objective function to evaluate the performance of a subcarrier assignment pattern and use a genetic algorithm to search through the huge solution space and select s best subcarrier assignment patterns based on the approximate objective values. In the second stage, we employ an off-line trained artificial neural network to estimate the objective values of the s subcarrier assignment patterns obtained in stage 1 and select the l best patterns. In the third stage, we use the exact objective function to evaluate the l subcarrier assignment patterns obtained in stage 2, and the best one associated with the corresponding optimal bit allocation is the good enough solution that we seek. We apply our algorithm to numerous cases of large-dimension ASABA problems and compare the results with those obtained by four existing algorithms. The test results show that our algorithm is the best in both aspects of solution quality and computational efficiency.
ER -