IEICE TRANSACTIONS on Fundamentals
Low-Cost Learning-Based Path Loss Estimation Using Correlation Graph CNN
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E106-A No.8 pp.1072-1076
- Publication Date
- 2023/08/01
- Publicized
- 2023/01/26
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2022EAL2094
- Type of Manuscript
- LETTER
- Category
- Communication Theory and Signals
Authors
Keita IMAIZUMI
Yokohama National University
Koichi ICHIGE
Yokohama National University
Tatsuya NAGAO
KDDI Research Inc.
Takahiro HAYASHI
KDDI Research Inc.
Keyword
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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
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@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},}
Copy
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 -
English
