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Paper Abstract and Keywords
Presentation 2025-07-30 10:25
Radio Propagation Prediction Based on Physics-Informed Graph Representation Learning
Shimon Takagi, Koya Sato (UEC), Katsuya Suto (Hokkaido Univ.) SR2025-17
Abstract (in Japanese) (See Japanese page) 
(in English) To achieve both high prediction accuracy and reduced computation time in radio propagation simulations, a novel concept called radio propagation graph representation learning has been proposed. However, existing implementations often build models tailored to the specific path loss characteristics present in the training data. As a result, their prediction accuracy significantly degrades when the path loss characteristics of the test data differ substantially from those of the training data. To address this issue, this paper proposes a framework for radio propagation graph representation learning capable of generalizing to arbitrary path loss environments. The proposed method represents paths with a graph structure while representing received power with both a physics-based mathematical model and data-driven learning. Performance evaluations using actual signal data with varying path loss exponents demonstrate that the proposed method can improve the root mean squared error of the predicted received power and reduce the computation time compared to conventional ray tracing.
Keyword (in Japanese) (See Japanese page) 
(in English) Radio propagation / Graph representation learning / Physics-informed machine learning / Ray tracing / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 134, SR2025-17, pp. 7-13, July 2025.
Paper # SR2025-17 
Date of Issue 2025-07-23 (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 SR2025-17

Conference Information
Committee SR RCS RCC SeMI NS RISING HCL  
Conference Date 2025-07-30 - 2025-08-01 
Place (in Japanese) (See Japanese page) 
Place (in English) MALIOS 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SR 
Conference Code 2025-07-SR-RCS-RCC-SeMI-NS-RISING-HCL 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Radio Propagation Prediction Based on Physics-Informed Graph Representation Learning 
Sub Title (in English)  
Keyword(1) Radio propagation  
Keyword(2) Graph representation learning  
Keyword(3) Physics-informed machine learning  
Keyword(4) Ray tracing  
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1st Author's Name Shimon Takagi  
1st Author's Affiliation The University of Electro-Communications (UEC)
2nd Author's Name Koya Sato  
2nd Author's Affiliation The University of Electro-Communications (UEC)
3rd Author's Name Katsuya Suto  
3rd Author's Affiliation Hokkaido University (Hokkaido Univ.)
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Speaker Author-1 
Date Time 2025-07-30 10:25:00 
Presentation Time 25 minutes 
Registration for SR 
Paper # SR2025-17 
Volume (vol) vol.125 
Number (no) no.134 
Page pp.7-13 
#Pages 7 
Date of Issue 2025-07-23 (SR) 

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