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Paper Abstract and Keywords
Presentation 2025-11-20 15:20
[Invited Lecture] Radio Propagation Estimation with Graph Representation Learning
Katsuya Suto (Hokkaido Univ.), Shimon Takagi (UEC) SRW2025-36
Abstract (in Japanese) (See Japanese page) 
(in English) We have proposed a radio propagation graph representation learning approach that enables highly accurate and computationally efficient estimation of outdoor radio propagation. A radio propagation graph represents transmitters, obstacles, and receivers as nodes, while edges correspond to propagation paths. The proposed graph representation learning framework constructs these nodes and edges by leveraging environmental information through machine learning models. In this talk, we introduce an edge construction method that considers uncertainties such as quantization errors in 3D maps and the scattered waves. Unlike conventional machine learning methods that require ground-truth edge data, the proposed approach jointly trains propagation path and received power estimation models in an end-to-end manner, using only received power as supervision. This enables the simultaneous optimization of propagation path and received power estimations. Simulation results based on real-world data demonstrate that our proposal achieves higher accuracy in received power estimation and significantly reduces computation time compared with the conventional ray-tracing approach.
Keyword (in Japanese) (See Japanese page) 
(in English) Radio propagation / graph representation learning / channel / ray-tracing / machine learning / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 256, SRW2025-36, pp. 41-41, Nov. 2025.
Paper # SRW2025-36 
Date of Issue 2025-11-13 (SRW) 
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 SRW2025-36

Conference Information
Committee SRW  
Conference Date 2025-11-20 - 2025-11-21 
Place (in Japanese) (See Japanese page) 
Place (in English) Kyoto Univ. Rakuyu-Kaikan Bldg. 2F Lecture room 
Topics (in Japanese) (See Japanese page) 
Topics (in English) IoT Workshop (General and Poster presentations regarding IoT techniques) 
Paper Information
Registration To SRW 
Conference Code 2025-11-SRW 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Radio Propagation Estimation with Graph Representation Learning 
Sub Title (in English)  
Keyword(1) Radio propagation  
Keyword(2) graph representation learning  
Keyword(3) channel  
Keyword(4) ray-tracing  
Keyword(5) machine learning  
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1st Author's Name Katsuya Suto  
1st Author's Affiliation Hokkaido University (Hokkaido Univ.)
2nd Author's Name Shimon Takagi  
2nd Author's Affiliation The University of Electro-Communications (UEC)
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Speaker Author-1 
Date Time 2025-11-20 15:20:00 
Presentation Time 25 minutes 
Registration for SRW 
Paper # SRW2025-36 
Volume (vol) vol.125 
Number (no) no.256 
Page p.41 
#Pages 1 
Date of Issue 2025-11-13 (SRW) 

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