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Paper Abstract and Keywords
Presentation 2025-02-21 15:15
[Invited Lecture] Evaluation of a Deep Learning Propagation Loss Estimation Model with Urban Characteristics Using Side-view Images
Ryotaro Taniguchi, Minoru Inomata, Wataru Yamada, Yasushi Takatori, Tomoaki Ogawa (NTT) AP2024-204
Abstract (in Japanese) (See Japanese page) 
(in English) We have investigated the path loss prediction model based on deep learning that does not require the derivation of complex functional forms and allows regression to arbitrary nonlinear functions. Then, focusing on the fact that the path loss estimation for urban macrocell (UMa) environment is dominated by the propagation over roof in the building between transmitting and receiving stations, we propose a new model that defines the building on the Tx-Rx straight line as the side-view image. In this paper, we confirm the applicability of the proposed method when the building density changes by reducing the number of buildings in the evaluation environment, and propose a method to select a path loss estimation model suitable for the target environment and improve the building reduction rate. Clarify its characteristics by evaluating the estimation error for
Keyword (in Japanese) (See Japanese page) 
(in English) Radio propagation / Deep learning / Path loss / / / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 379, AP2024-204, pp. 79-84, Feb. 2025.
Paper # AP2024-204 
Date of Issue 2025-02-13 (AP) 
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 AP2024-204

Conference Information
Committee AP  
Conference Date 2025-02-20 - 2025-02-21 
Place (in Japanese) (See Japanese page) 
Place (in English) Toyohashi Univ. of Technology 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Industrial Session, Antennas and Propagation 
Paper Information
Registration To AP 
Conference Code 2025-02-AP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Evaluation of a Deep Learning Propagation Loss Estimation Model with Urban Characteristics Using Side-view Images 
Sub Title (in English)  
Keyword(1) Radio propagation  
Keyword(2) Deep learning  
Keyword(3) Path loss  
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1st Author's Name Ryotaro Taniguchi  
1st Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
2nd Author's Name Minoru Inomata  
2nd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
3rd Author's Name Wataru Yamada  
3rd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
4th Author's Name Yasushi Takatori  
4th Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
5th Author's Name Tomoaki Ogawa  
5th Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
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Speaker Author-1 
Date Time 2025-02-21 15:15:00 
Presentation Time 25 minutes 
Registration for AP 
Paper # AP2024-204 
Volume (vol) vol.124 
Number (no) no.379 
Page pp.79-84 
#Pages 6 
Date of Issue 2025-02-13 (AP) 

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