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| Presentation | 2021-03-03 09:45 Performance Evaluation of a Machine Learning-Based Rate Selection Scheme for Wireless LAN in the Presence of Contention and Hidden Nodes Souki Watanabe, Hiraku Okada, Ben Naila Chedlia, Masaaki Katayama (Nagoya Univ.)  RCS2020-203 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In IEEE802.11, the transmission rate is specified in multiple steps by the Modulation and Coding Scheme (MCS), which is an index of combinations of modulation schemes and coding rates. In order to maximize throughput, it is necessary to select an appropriate transmission rate for the communication environment. However, the actual communication environment is complicated by many factors, such as the effects of fading, contention by other communications, and hidden terminal problem. In our previous study, we investigated a rate adaptation method for complex communication environments by applying machine learning. However, only one-to-one communication experiments were conducted, and the results were not sufficiently compared with those of conventional methods. In this paper, we show the superiority of the proposed method by comparing and verifying the transmission rate selection using LightGBM and the conventional method in the presence of competing communications. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | IEEE 802.11 / machine learning / rate adaptation / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 120, no. 404, RCS2020-203, pp. 1-6, March 2021. | |
| Paper # | RCS2020-203 | |
| Date of Issue | 2021-02-24 (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 | RCS2020-203 |
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| Conference Information | |
| Committee | RCS SR SRW |
| Conference Date | 2021-03-03 - 2021-03-05 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Mobile Communication Workshop |
| Paper Information | |
| Registration To | RCS |
| Conference Code | 2021-03-RCS-SR-SRW |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Performance Evaluation of a Machine Learning-Based Rate Selection Scheme for Wireless LAN in the Presence of Contention and Hidden Nodes |
| Sub Title (in English) | |
| Keyword(1) | IEEE 802.11 |
| Keyword(2) | machine learning |
| Keyword(3) | rate adaptation |
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| 1st Author's Name | Souki Watanabe |
| 1st Author's Affiliation | Nagoya University (Nagoya Univ.) |
| 2nd Author's Name | Hiraku Okada |
| 2nd Author's Affiliation | Nagoya University (Nagoya Univ.) |
| 3rd Author's Name | Ben Naila Chedlia |
| 3rd Author's Affiliation | Nagoya University (Nagoya Univ.) |
| 4th Author's Name | Masaaki Katayama |
| 4th Author's Affiliation | Nagoya University (Nagoya Univ.) |
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| Speaker | Author-1 |
| Date Time | 2021-03-03 09:45:00 |
| Presentation Time | 25 minutes |
| Registration for | RCS |
| Paper # | RCS2020-203 |
| Volume (vol) | vol.120 |
| Number (no) | no.404 |
| Page | pp.1-6 |
| #Pages | 6 |
| Date of Issue | 2021-02-24 (RCS) |
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