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 cette lettre, un réseau neuronal (NN) pour la réduction de puissance de crête d'un signal de multiplexage par répartition orthogonale de la fréquence (OFDM) est amélioré afin de supprimer sa complexité de calcul. Des expériences numériques montrent que la quantité d'IFFT dans le NN proposé peut être réduite de moitié et que son temps de calcul peut être réduit de 21.5 % par rapport à un NN conventionnel. Par rapport au SLM, le NN proposé est efficace pour obtenir une réduction élevée du PAPR et présente un avantage par rapport au SLM dans des conditions de calcul égales.
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Masaya OHTA, Keiichi MIZUTANI, Katsumi YAMASHITA, "Complexity Suppression of Neural Networks for PAPR Reduction of OFDM Signal" in IEICE TRANSACTIONS on Fundamentals,
vol. E93-A, no. 9, pp. 1704-1708, September 2010, doi: 10.1587/transfun.E93.A.1704.
Abstract: In this letter, a neural network (NN) for peak power reduction of an orthogonal frequency-division multiplexing (OFDM) signal is improved in order to suppress its computational complexity. Numerical experiments show that the amount of IFFTs in the proposed NN can be reduced to half, and its computational time can be reduced by 21.5% compared with a conventional NN. In comparison with the SLM, the proposed NN is effective to achieve high PAPR reduction and it has an advantage over the SLM under the equal computational condition.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E93.A.1704/_p
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@ARTICLE{e93-a_9_1704,
author={Masaya OHTA, Keiichi MIZUTANI, Katsumi YAMASHITA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Complexity Suppression of Neural Networks for PAPR Reduction of OFDM Signal},
year={2010},
volume={E93-A},
number={9},
pages={1704-1708},
abstract={In this letter, a neural network (NN) for peak power reduction of an orthogonal frequency-division multiplexing (OFDM) signal is improved in order to suppress its computational complexity. Numerical experiments show that the amount of IFFTs in the proposed NN can be reduced to half, and its computational time can be reduced by 21.5% compared with a conventional NN. In comparison with the SLM, the proposed NN is effective to achieve high PAPR reduction and it has an advantage over the SLM under the equal computational condition.},
keywords={},
doi={10.1587/transfun.E93.A.1704},
ISSN={1745-1337},
month={September},}
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TY - JOUR
TI - Complexity Suppression of Neural Networks for PAPR Reduction of OFDM Signal
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1704
EP - 1708
AU - Masaya OHTA
AU - Keiichi MIZUTANI
AU - Katsumi YAMASHITA
PY - 2010
DO - 10.1587/transfun.E93.A.1704
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E93-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2010
AB - In this letter, a neural network (NN) for peak power reduction of an orthogonal frequency-division multiplexing (OFDM) signal is improved in order to suppress its computational complexity. Numerical experiments show that the amount of IFFTs in the proposed NN can be reduced to half, and its computational time can be reduced by 21.5% compared with a conventional NN. In comparison with the SLM, the proposed NN is effective to achieve high PAPR reduction and it has an advantage over the SLM under the equal computational condition.
ER -