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Paper Abstract and Keywords
Presentation 2021-03-04 11:35
[Poster Presentation] Radio Propagation Prediction Using Building Information in Radio Environment Map for Smart Meters
Soraya Mitate, Takeo Fujii (UEC) RCS2020-237 SR2020-76 SRW2020-66
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, smart metering systems that efficiently control household resources such as water, electricity, and gas have attracted to increase the demand for reducing infrastructure operating costs. Each smart meter transmits the measured data to the Fusion Center (FC) via wireless multi-hop communication in a smart metering system. This means that new Smart Meters must be installed within the range where a wireless connection can be guaranteed to surrounding Smart Meters. Hence, installing a new smart meter is essential to obtain the radio propagation characteristics around the installation location. On the other hand, smart meters are installed in the corner of a house, and their height is lower than the height of the surrounding buildings. Therefore, it is challenging to adopt the empirical propagation model because the buildings around the terminal affect the communication between terminals. Given the above background, this paper aims to develop a highly accurate radio propagation prediction for smart meter systems installed in residential areas. We propose a combined NN (Neural network) to achieve this goal. The proposed NN consists of an NN that learns from parameters such as the positions at smart meters, a CNN that extracts features from satellite images around the terminals, and an NN that learns from the NN’s output in the previous stage. The proposed method reduces the standard deviation of the estimation error distribution by 3.02[dB] compared to the empirical model reported in ITU-R P.1411.
Keyword (in Japanese) (See Japanese page) 
(in English) Smart meters / Radio propagation prediction / Radio environment map / Machine learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 404, RCS2020-237, pp. 163-164, March 2021.
Paper # RCS2020-237 
Date of Issue 2021-02-24 (RCS, SR, SRW) 
ISSN 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)
Download PDF RCS2020-237 SR2020-76 SRW2020-66

Conference Information
Committee RCS SR SRW  
Conference Date 2021-03-03 - 2021-03-05 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Mobile Communication Workshop 
Paper Information
Registration To RCS 
Conference Code 2021-03-RCS-SR-SRW 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Radio Propagation Prediction Using Building Information in Radio Environment Map for Smart Meters 
Sub Title (in English)  
Keyword(1) Smart meters  
Keyword(2) Radio propagation prediction  
Keyword(3) Radio environment map  
Keyword(4) Machine learning  
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1st Author's Name Soraya Mitate  
1st Author's Affiliation The University of Electro-Communications (UEC)
2nd Author's Name Takeo Fujii  
2nd Author's Affiliation The University of Electro-Communications (UEC)
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Speaker Author-1 
Date Time 2021-03-04 11:35:00 
Presentation Time 40 minutes 
Registration for RCS 
Paper # RCS2020-237, SR2020-76, SRW2020-66 
Volume (vol) vol.120 
Number (no) no.404(RCS), no.405(SR), no.406(SRW) 
Page pp.163-164(RCS), pp.56-57(SR), pp.51-52(SRW) 
#Pages
Date of Issue 2021-02-24 (RCS, SR, SRW) 


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