Paper Abstract and Keywords |
Presentation |
2019-01-24 15:45
On the Radio Environment Map Construction using Neural Network Residual Kriging Koya Sato (TUS), Kei Inage (TMCIT), Takeo Fujii (UEC) SR2018-106 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In this paper, we discuss the performance of feedforward neural network (FFNN) in radio environment map (REM) construction. We can realize a highly accurate REM via croudsourcing with Kriging. In the most works on the Kriging-aided REM construction, the measurement datasets are first regressed via linear regression in order to ensure spatial stationarity of the random variable. On the other hand, the path loss in the practical situation often contains an anisotropy due to effects of terrain and obstacles; thus, Kriging may not perform the optimal interpolation because of the regression error. In this paper, FFNN is used for the path loss modeling and the regression. Through theoretical, numerical and experimental discussions, we show situations where the FFNN can improve the accuracy of REM. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
machine learning / neural network / regression analysis / spatial interpolation / radio environment map / crowdsensing / / |
Reference Info. |
IEICE Tech. Rep., vol. 118, no. 421, SR2018-106, pp. 63-70, Jan. 2019. |
Paper # |
SR2018-106 |
Date of Issue |
2019-01-17 (SR) |
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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SR2018-106 |
Conference Information |
Committee |
SR |
Conference Date |
2019-01-24 - 2019-01-25 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Corasse, Fukushima city (Fukushima prefecture) |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
cognitive radio, machine learning application, heterogeneous network, SDN, IoT etc. |
Paper Information |
Registration To |
SR |
Conference Code |
2019-01-SR |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
On the Radio Environment Map Construction using Neural Network Residual Kriging |
Sub Title (in English) |
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Keyword(1) |
machine learning |
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neural network |
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regression analysis |
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spatial interpolation |
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radio environment map |
Keyword(6) |
crowdsensing |
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1st Author's Name |
Koya Sato |
1st Author's Affiliation |
Tokyo University of Science (TUS) |
2nd Author's Name |
Kei Inage |
2nd Author's Affiliation |
Tokyo Metropolitan College of Industrial Technology (TMCIT) |
3rd Author's Name |
Takeo Fujii |
3rd Author's Affiliation |
The University of Electro-Communications (UEC) |
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Speaker |
Author-1 |
Date Time |
2019-01-24 15:45:00 |
Presentation Time |
25 minutes |
Registration for |
SR |
Paper # |
SR2018-106 |
Volume (vol) |
vol.118 |
Number (no) |
no.421 |
Page |
pp.63-70 |
#Pages |
8 |
Date of Issue |
2019-01-17 (SR) |
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