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Paper Abstract and Keywords
Presentation 2020-11-27 11:50
[Invited Lecture] Two-step Path Loss Prediction Method by Artificial Neural Network for Wireless Service Area Planning
Kentaro Saito, Yongri JIN, CheChia Kang, Jun-ichi Takada (Tokyo Tech.) SRW2020-38
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, wireless network systems are utilized in various industry fields and the wireless service area planning became one of the important tasks to realize efficient and high-quality wireless communication service. The machine learning technology attracts the interests of researchers to improve the efficiency of the area planning task because the radio propagation loss in unknown locations can be predicted by the training data without explicit algorithms. Our previous work showed that the path loss (PL) characteristics become complicated in the high PL region, and it can degrade the entire prediction accuracy. In this paper, we propose the two-step PL prediction method by the artificial neural network (ANN) to solve the issue. Firstly, the area is classified into several zones according to the PL range. And then the PL is predicted by ANNs that were trained for respective zones. Our proposal was evaluated by the ray-tracing simulation data, and the result showed that it improved the root mean square error (RMSE) of PL prediction from 7.9 dB to 4.1 dB. The method is expected to be utilized for the wireless service area planning in various environments.
Keyword (in Japanese) (See Japanese page) 
(in English) Cell planning / Indoor propagation / Machine learning / Neural network / Path loss prediction / propagation simulation / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 260, SRW2020-38, pp. 61-66, Nov. 2020.
Paper # SRW2020-38 
Date of Issue 2020-11-19 (SRW) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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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)
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Conference Information
Committee SRW SeMI CNR  
Conference Date 2020-11-26 - 2020-11-27 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) IoT Workshop 
Paper Information
Registration To SRW 
Conference Code 2020-11-SRW-SeMI-CNR 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Two-step Path Loss Prediction Method by Artificial Neural Network for Wireless Service Area Planning 
Sub Title (in English)  
Keyword(1) Cell planning  
Keyword(2) Indoor propagation  
Keyword(3) Machine learning  
Keyword(4) Neural network  
Keyword(5) Path loss prediction  
Keyword(6) propagation simulation  
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Keyword(8)  
1st Author's Name Kentaro Saito  
1st Author's Affiliation Tokyo Institute of Technology (Tokyo Tech.)
2nd Author's Name Yongri JIN  
2nd Author's Affiliation Tokyo Institute of Technology (Tokyo Tech.)
3rd Author's Name CheChia Kang  
3rd Author's Affiliation Tokyo Institute of Technology (Tokyo Tech.)
4th Author's Name Jun-ichi Takada  
4th Author's Affiliation Tokyo Institute of Technology (Tokyo Tech.)
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Speaker
Date Time 2020-11-27 11:50:00 
Presentation Time 20 
Registration for SRW 
Paper # IEICE-SRW2020-38 
Volume (vol) IEICE-120 
Number (no) no.260 
Page pp.61-66 
#Pages IEICE-6 
Date of Issue IEICE-SRW-2020-11-19 


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