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| Presentation | 2019-01-28 14:40 Deep Reinforcement Learning-Based Optimum Channel Control for Wireless LAN Kota Nakashima, Syotaro Kamiya, Kazuki Ohtsu, Koji Yamamoto, Takayuki Nishio, Masahiro Morikura (Kyoto Univ.)  ASN2018-80 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | This report proposes deep reinforcement learning-based channel selection method when access points (APs) are located densely. In densely deployed WLANs, APs could have many APs in their carrier sensing range and throughput of the APs becomes low due to high contention. We apply graph convolution networks (GCN) to a contention graph where APs in their carrier sense range are connected for extracting the features of carrier sensing relationship. Moreover, by selecting an action according to spatial adaptive play (SAP) method, we improve the learning efficiency. The simulation results show that the proposal method can control the channels appropriately in comparison to other methods. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | deep reinforcement learning / graph convolutional networks / spatial adaptive play / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 118, no. 428, ASN2018-80, pp. 13-18, Jan. 2019. | |
| Paper # | ASN2018-80 | |
| Date of Issue | 2019-01-21 (ASN) | |
| 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) | |
| Download PDF | ASN2018-80 |
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| Conference Information | |
| Committee | ASN |
| Conference Date | 2019-01-28 - 2019-01-29 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Kyuukamura Ibusuki |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Ambient intelligence, Sensor networks, Poster session, etc. |
| Paper Information | |
| Registration To | ASN |
| Conference Code | 2019-01-ASN |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Deep Reinforcement Learning-Based Optimum Channel Control for Wireless LAN |
| Sub Title (in English) | |
| Keyword(1) | deep reinforcement learning |
| Keyword(2) | graph convolutional networks |
| Keyword(3) | spatial adaptive play |
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| 1st Author's Name | Kota Nakashima |
| 1st Author's Affiliation | Kyoto University (Kyoto Univ.) |
| 2nd Author's Name | Syotaro Kamiya |
| 2nd Author's Affiliation | Kyoto University (Kyoto Univ.) |
| 3rd Author's Name | Kazuki Ohtsu |
| 3rd Author's Affiliation | Kyoto University (Kyoto Univ.) |
| 4th Author's Name | Koji Yamamoto |
| 4th Author's Affiliation | Kyoto University (Kyoto Univ.) |
| 5th Author's Name | Takayuki Nishio |
| 5th Author's Affiliation | Kyoto University (Kyoto Univ.) |
| 6th Author's Name | Masahiro Morikura |
| 6th Author's Affiliation | Kyoto University (Kyoto Univ.) |
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| Speaker | Author-1 |
| Date Time | 2019-01-28 14:40:00 |
| Presentation Time | 25 minutes |
| Registration for | ASN |
| Paper # | ASN2018-80 |
| Volume (vol) | vol.118 |
| Number (no) | no.428 |
| Page | pp.13-18 |
| #Pages | 6 |
| Date of Issue | 2019-01-21 (ASN) |
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