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| Presentation | 2020-03-05 09:25 Deep reinforcement learning based access control scheme for radio access networks Hang Zhou, Xiaoyan Wang, Masahiro Umehira (Ibaraki Univ.)  SR2019-123 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | After a disaster occurred, it is extremely important to reconstruct the network and provide the communication services to the victims immediately. Deploying MDRU (movable and deployable resource unit) in the disaster area, along with multiple access points to extend the service area of MDRU is a very promising solution. In the disaster area, since the communication environment changes frequently, it is difficult for the user terminal to perform the optimal radio access control. In this paper, we propose a deep reinforcement learning based radio access control mechanism, and evaluate its performance by simulations. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Deep reinforcement learning / Radio access network / / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 449, SR2019-123, pp. 59-64, March 2020. | |
| Paper # | SR2019-123 | |
| Date of Issue | 2020-02-26 (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) | |
| Download PDF | SR2019-123 |
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| Conference Information | |
| Committee | RCS SR SRW |
| Conference Date | 2020-03-04 - 2020-03-06 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Tokyo Institute of Technology |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Mobile Communication Workshop |
| Paper Information | |
| Registration To | SR |
| Conference Code | 2020-03-RCS-SR-SRW |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Deep reinforcement learning based access control scheme for radio access networks |
| Sub Title (in English) | |
| Keyword(1) | Deep reinforcement learning |
| Keyword(2) | Radio access network |
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| 1st Author's Name | Hang Zhou |
| 1st Author's Affiliation | Graduate School of Ibaraki University (Ibaraki Univ.) |
| 2nd Author's Name | Xiaoyan Wang |
| 2nd Author's Affiliation | Graduate School of Ibaraki University (Ibaraki Univ.) |
| 3rd Author's Name | Masahiro Umehira |
| 3rd Author's Affiliation | Graduate School of Ibaraki University (Ibaraki Univ.) |
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| Speaker | Author-1 |
| Date Time | 2020-03-05 09:25:00 |
| Presentation Time | 25 minutes |
| Registration for | SR |
| Paper # | SR2019-123 |
| Volume (vol) | vol.119 |
| Number (no) | no.449 |
| Page | pp.59-64 |
| #Pages | 6 |
| Date of Issue | 2020-02-26 (SR) |
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