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Paper Abstract and Keywords
Presentation 2023-02-16 13:50
[Invited Lecture] Deep-Learning Path Loss Prediction Model Using Side-View Images Considering Frequency Characteristics
Nobuaki Kuno, Minoru Inomata, Motoharu Sasaki, Wataru Yamada (NTT) AP2022-216
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 focus on the consideration of the frequency characteristics by the side-view image, and compare it with the conventional model using the top-view image of the building around the receiving station. In RMS error verification using measured data, the proposed model has an estimation error of 4.4 dB compared to 9.8 dB in the conventional model.
Keyword (in Japanese) (See Japanese page) 
(in English) Radio propagation / Deep learning / Path loss / / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 378, AP2022-216, pp. 106-110, Feb. 2023.
Paper # AP2022-216 
Date of Issue 2023-02-08 (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 AP2022-216

Conference Information
Committee AP  
Conference Date 2023-02-15 - 2023-02-17 
Place (in Japanese) (See Japanese page) 
Place (in English) Tohoku University, Aobayama Campus 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Industrial Session, Antennas and Propagation 
Paper Information
Registration To AP 
Conference Code 2023-02-AP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Deep-Learning Path Loss Prediction Model Using Side-View Images Considering Frequency Characteristics 
Sub Title (in English)  
Keyword(1) Radio propagation  
Keyword(2) Deep learning  
Keyword(3) Path loss  
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1st Author's Name Nobuaki Kuno  
1st Author's Affiliation NTT (NTT)
2nd Author's Name Minoru Inomata  
2nd Author's Affiliation NTT (NTT)
3rd Author's Name Motoharu Sasaki  
3rd Author's Affiliation NTT (NTT)
4th Author's Name Wataru Yamada  
4th Author's Affiliation NTT (NTT)
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Speaker Author-1 
Date Time 2023-02-16 13:50:00 
Presentation Time 25 minutes 
Registration for AP 
Paper # AP2022-216 
Volume (vol) vol.122 
Number (no) no.378 
Page pp.106-110 
#Pages 5 
Date of Issue 2023-02-08 (AP) 

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