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| 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 |
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| 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 | |
| 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) | |
| Download PDF | MoNA2017-52 |
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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 |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 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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| Speaker | Author-1 |
| Date Time | 2018-01-19 11:40:00 |
| Presentation Time | 25 minutes |
| Registration for | MoNA |
| Paper # | MoNA2017-52 |
| Volume (vol) | vol.117 |
| Number (no) | no.390 |
| Page | pp.63-68 |
| #Pages | 6 |
| Date of Issue | 2018-01-11 (MoNA) |
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