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| Paper Abstract and Keywords | ||
| Presentation | 2023-07-12 13:55 Radio Propagation Graph Representation Learning Katsuya Suto, Shinsuke Bannai, Koya Sato, Takeo Fujii (UEC)  SR2023-29 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In the paper, we propose a novel concept of data-driven radio propagation estimation, referred to as radio propagation graph representation learning. In the concept, radio propagation is represented by a graph, where nodes represent the transmitter, receiver, and obstruction, and edges express the propagation between nodes. Using the model, we can represent a complex radio propagation, including reflection, diffraction, and shielding. Further, we can achieve fast propagation estimation using a shallow feed-forward neural network. Through the simulation using actual datasets in urban areas, we demonstrate that the proposal achieves twenty times faster computation than ray tracing while predicting shadowing well. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Radio propagation graph / Ray tracing / Machine learning / 3D urban model / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 123, no. 109, SR2023-29, pp. 19-24, July 2023. | |
| Paper # | SR2023-29 | |
| Date of Issue | 2023-07-05 (SR) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | SR2023-29 |
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| Conference Information | |
| Committee | SeMI RCS RCC NS SR |
| Conference Date | 2023-07-12 - 2023-07-14 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Osaka University Nakanoshima Center + Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Distributed Wireless Network, M2M (Machine-to-Machine)，D2D (Device-to-Device)，IoT（Internet of Things）, etc |
| Paper Information | |
| Registration To | SR |
| Conference Code | 2023-07-SeMI-RCS-RCC-NS-SR |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Radio Propagation Graph Representation Learning |
| Sub Title (in English) | |
| Keyword(1) | Radio propagation graph |
| Keyword(2) | Ray tracing |
| Keyword(3) | Machine learning |
| Keyword(4) | 3D urban model |
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| 1st Author's Name | Katsuya Suto |
| 1st Author's Affiliation | The University of Electro-Communications (UEC) |
| 2nd Author's Name | Shinsuke Bannai |
| 2nd Author's Affiliation | The University of Electro-Communications (UEC) |
| 3rd Author's Name | Koya Sato |
| 3rd Author's Affiliation | The University of Electro-Communications (UEC) |
| 4th Author's Name | Takeo Fujii |
| 4th Author's Affiliation | The University of Electro-Communications (UEC) |
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| Speaker | Author-1 |
| Date Time | 2023-07-12 13:55:00 |
| Presentation Time | 25 minutes |
| Registration for | SR |
| Paper # | SR2023-29 |
| Volume (vol) | vol.123 |
| Number (no) | no.109 |
| Page | pp.19-24 |
| #Pages | 6 |
| Date of Issue | 2023-07-05 (SR) |
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