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
본 논문에서는 상관 그래프 컨볼루션 신경망(C-Graph CNN)을 이용하여 전파 전파를 예측하는 방법을 제안한다. C-Graph CNN에서 시스템 매개변수로 어떤 매개변수를 사용하기에 적합한지 살펴봅니다. 제안된 방법의 성능은 시뮬레이션을 통한 경로 손실 추정 정확도와 계산 비용을 통해 평가된다.
Keita IMAIZUMI
Yokohama National University
Koichi ICHIGE
Yokohama National University
Tatsuya NAGAO
KDDI Research Inc.
Takahiro HAYASHI
KDDI Research Inc.
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부
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
부
@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},}
부
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 -