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 |
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) |
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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) |
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Smart meters |
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Radio propagation prediction |
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Radio environment map |
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Machine learning |
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1st Author's Name |
Soraya Mitate |
1st Author's Affiliation |
The University of Electro-Communications (UEC) |
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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 |
2 |
Date of Issue |
2021-02-24 (RCS, SR, SRW) |
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