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
Dalam makalah ini, kami mencadangkan kaedah untuk meramalkan perambatan gelombang radio menggunakan rangkaian neural convolutional graf korelasi (C-Graph CNN). Kami meneliti jenis parameter yang sesuai digunakan sebagai parameter sistem dalam C-Graph CNN. Prestasi kaedah yang dicadangkan dinilai oleh ketepatan anggaran kehilangan laluan dan kos pengiraan melalui simulasi.
Keita IMAIZUMI
Yokohama National University
Koichi ICHIGE
Yokohama National University
Tatsuya NAGAO
KDDI Research Inc.
Takahiro HAYASHI
KDDI Research Inc.
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Salinan
Keita IMAIZUMI, Koichi ICHIGE, Tatsuya NAGAO, Takahiro HAYASHI, "Low-Cost Learning-Based Path Loss Estimation Using Correlation Graph CNN" in IEICE TRANSACTIONS on Fundamentals,
vol. E106-A, no. 8, pp. 1072-1076, August 2023, doi: 10.1587/transfun.2022EAL2094.
Abstract: In this paper, we propose a method for predicting radio wave propagation using a correlation graph convolutional neural network (C-Graph CNN). We examine what kind of parameters are suitable to be used as system parameters in C-Graph CNN. Performance of the proposed method is evaluated by the path loss estimation accuracy and the computational cost through simulation.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2022EAL2094/_p
Salinan
@ARTICLE{e106-a_8_1072,
author={Keita IMAIZUMI, Koichi ICHIGE, Tatsuya NAGAO, Takahiro HAYASHI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Low-Cost Learning-Based Path Loss Estimation Using Correlation Graph CNN},
year={2023},
volume={E106-A},
number={8},
pages={1072-1076},
abstract={In this paper, we propose a method for predicting radio wave propagation using a correlation graph convolutional neural network (C-Graph CNN). We examine what kind of parameters are suitable to be used as system parameters in C-Graph CNN. Performance of the proposed method is evaluated by the path loss estimation accuracy and the computational cost through simulation.},
keywords={},
doi={10.1587/transfun.2022EAL2094},
ISSN={1745-1337},
month={August},}
Salinan
TY - JOUR
TI - Low-Cost Learning-Based Path Loss Estimation Using Correlation Graph CNN
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1072
EP - 1076
AU - Keita IMAIZUMI
AU - Koichi ICHIGE
AU - Tatsuya NAGAO
AU - Takahiro HAYASHI
PY - 2023
DO - 10.1587/transfun.2022EAL2094
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E106-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 2023
AB - In this paper, we propose a method for predicting radio wave propagation using a correlation graph convolutional neural network (C-Graph CNN). We examine what kind of parameters are suitable to be used as system parameters in C-Graph CNN. Performance of the proposed method is evaluated by the path loss estimation accuracy and the computational cost through simulation.
ER -