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Paper Abstract and Keywords
Presentation 2018-01-19 11:40
Machine Learning-based Primary Exclusive Region Update for Spectrum Sharing
Aogu Yamada, Takayuki Nishio, Masahiro Morikura, Koji Yamamoto (Kyoto Univ.) MoNA2017-52
Abstract (in Japanese) (See Japanese page) 
(in English) In spectrum sharing, secondary users (SUs) utilize a licensed frequency band under a condition to avoid interference with a primary user (PU), such as a primary exclusive region (PER), wherein the SUs are prohibited to transmit. However, when SUs outside the PER interfere with the PU due to long term variation of propagation paths, the PER needs to be updated. In this paper, we propose a framework for updating the PER adaptively with supervised learning, when interference occurs. This frameworks employs training data sets, which are transmission logs of SUs with supervised labels whether or not PU detects interference from SUs. However, dominance of the non-interference labels compared to the interference labels degrades learning performance. To overcome imbalance of the supervised labels, we designed a novel sampling schemes considering propagation characteristics. Simulation results show the proposed framework continues to reduce interference probability and achieves smaller PER with fewer iterations compared to the conventional frameworks.
Keyword (in Japanese) (See Japanese page) 
(in English) Machine learning / Spectrum sharing / Cognitive radio / Supervised learning / Imbalanced data / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 390, MoNA2017-52, pp. 63-68, Jan. 2018.
Paper # MoNA2017-52 
Date of Issue 2018-01-11 (MoNA) 
ISSN Print edition: ISSN 0913-5685  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 MoNA  
Conference Date 2018-01-18 - 2018-01-19 
Place (in Japanese) (See Japanese page) 
Place (in English) Campus Plaza Kyoto 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Mobile Network, Application of Machine Learning, Mobile Data, etc. 
Paper Information
Registration To MoNA 
Conference Code 2018-01-MoNA 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Machine Learning-based Primary Exclusive Region Update for Spectrum Sharing 
Sub Title (in English)  
Keyword(1) Machine learning  
Keyword(2) Spectrum sharing  
Keyword(3) Cognitive radio  
Keyword(4) Supervised learning  
Keyword(5) Imbalanced data  
1st Author's Name Aogu Yamada  
1st Author's Affiliation Kyoto University (Kyoto Univ.)
2nd Author's Name Takayuki Nishio  
2nd Author's Affiliation Kyoto University (Kyoto Univ.)
3rd Author's Name Masahiro Morikura  
3rd Author's Affiliation Kyoto University (Kyoto Univ.)
4th Author's Name Koji Yamamoto  
4th Author's Affiliation Kyoto University (Kyoto Univ.)
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Date Time 2018-01-19 11:40:00 
Presentation Time 25 
Registration for MoNA 
Paper # IEICE-MoNA2017-52 
Volume (vol) IEICE-117 
Number (no) no.390 
Page pp.63-68 
#Pages IEICE-6 
Date of Issue IEICE-MoNA-2018-01-11 

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