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| Paper Abstract and Keywords | ||
| Presentation | 2019-03-08 09:50 [Invited Lecture] A Reinforcement Learning Framework for User-to-Access Points Association in Future Wireless Networks Megumi Kaneko, Thi Ha Ly Dinh (NII), Keisuke Wakao, Hirantha Abeysekera, Yasushi Takatori (NTT)  RCS2018-322 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | This work investigates the issue of distributed user-to-multiple Acess Points (AP) association, where a user requiring several applications may be served by several APs simultaneously. The problem is formulated as a network sum-rate maximization subject to the required QoS constraints for each user and application, and AP load constraints. We propose a distributed user-to-multiple AP association method, where each user can take an optimized decision locally, leveraging reinforcement learning. Simulation results show that, compared to baseline schemes, the proposed method enables large throughput enhancements while satisfying the stringent and diverse user Quality of Service (QoS) requirements as well as AP load limitations, thereby reducing user outage probabilities. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Wireless Networks / WLAN / Reinforcement Learning / Q-Learning / user-to-AP association / sum-rate maximization / QoS constraints / | |
| Reference Info. | IEICE Tech. Rep., vol. 118, no. 474, RCS2018-322, pp. 205-205, March 2019. | |
| Paper # | RCS2018-322 | |
| Date of Issue | 2019-02-27 (RCS) | |
| 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 | RCS2018-322 |
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| Conference Information | |
| Committee | RCS SR SRW |
| Conference Date | 2019-03-06 - 2019-03-08 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | YRP |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Mobile Communication Workshop |
| Paper Information | |
| Registration To | RCS |
| Conference Code | 2019-03-RCS-SR-SRW |
| Language | English (Japanese title is available) |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Reinforcement Learning Framework for User-to-Access Points Association in Future Wireless Networks |
| Sub Title (in English) | |
| Keyword(1) | Wireless Networks |
| Keyword(2) | WLAN |
| Keyword(3) | Reinforcement Learning |
| Keyword(4) | Q-Learning |
| Keyword(5) | user-to-AP association |
| Keyword(6) | sum-rate maximization |
| Keyword(7) | QoS constraints |
| Keyword(8) | |
| 1st Author's Name | Megumi Kaneko |
| 1st Author's Affiliation | National Institute of Informatics (NII) |
| 2nd Author's Name | Thi Ha Ly Dinh |
| 2nd Author's Affiliation | National Institute of Informatics (NII) |
| 3rd Author's Name | Keisuke Wakao |
| 3rd Author's Affiliation | NTT Corporation (NTT) |
| 4th Author's Name | Hirantha Abeysekera |
| 4th Author's Affiliation | NTT Corporation (NTT) |
| 5th Author's Name | Yasushi Takatori |
| 5th Author's Affiliation | NTT Corporation (NTT) |
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| Speaker | Author-1 |
| Date Time | 2019-03-08 09:50:00 |
| Presentation Time | 25 minutes |
| Registration for | RCS |
| Paper # | RCS2018-322 |
| Volume (vol) | vol.118 |
| Number (no) | no.474 |
| Page | p.205 |
| #Pages | 1 |
| Date of Issue | 2019-02-27 (RCS) |
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