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Paper Abstract and Keywords
Presentation 2019-01-24 16:35
Highly Accurate Estimation of Radio Propagation using Model Classifier
Keita Katagiri, Keita Onose (UEC), Koya Sato (TUS), Kei Inage (TMCIT), Takeo Fujii (UEC) SR2018-108
Abstract (in Japanese) (See Japanese page) 
(in English) A Measurement-based Spectrum Database (MSD) attracts attention as highly accurate radio environment recognition. In the MSD, radio environment information is gathered by huge numbers of terminals and the gathered datasets are used to generate the Radio Environment Map (REM). However, the MSD stores the statistical information of each receiver mesh, so the registered data size is enormous. In this paper, we propose a method of classifying the propagation model with considering the shadowing fluctuation in a mesh under the fixed transmitter location environment, and unify the model at points where propagation characteristics are similar. We evaluate the proposed method by using the datasets measured at 3.5GHz cellular band. The results show that the proposed method can accurately estimate the radio environment while greatly reducing the registered data size. Furthermore, we study transmission power control using the model classifier. By designing the transmission power that satisfies the desired outage probability for the desired received power, we can confirm that communication efficiency is improved.
Keyword (in Japanese) (See Japanese page) 
(in English) Spectrum Database / Classify / Radio Propagation / / / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 421, SR2018-108, pp. 79-84, Jan. 2019.
Paper # SR2018-108 
Date of Issue 2019-01-17 (SR) 
ISSN Online edition: ISSN 2432-6380
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 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) Highly Accurate Estimation of Radio Propagation using Model Classifier 
Sub Title (in English)  
Keyword(1) Spectrum Database  
Keyword(2) Classify  
Keyword(3) Radio Propagation  
1st Author's Name Keita Katagiri  
1st Author's Affiliation The University of Electro-Communications (UEC)
2nd Author's Name Keita Onose  
2nd Author's Affiliation The University of Electro-Communications (UEC)
3rd Author's Name Koya Sato  
3rd Author's Affiliation Tokyo University of Science (TUS)
4th Author's Name Kei Inage  
4th Author's Affiliation Tokyo Metropolitan College of Industrial Technology (TMCIT)
5th Author's Name Takeo Fujii  
5th Author's Affiliation The University of Electro-Communications (UEC)
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Speaker Author-1 
Date Time 2019-01-24 16:35:00 
Presentation Time 25 minutes 
Registration for SR 
Paper # SR2018-108 
Volume (vol) vol.118 
Number (no) no.421 
Page pp.79-84 
Date of Issue 2019-01-17 (SR) 

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