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| Presentation | 2019-06-19 15:35 A Study on Application of Machine Learning to Transmission Rate Selection in Wireless Mesh Networks Soukiw Watanabe, Hiraku Okada, Kentaro Kobayashi, Masaaki Katayama (Nagoya Univ.)  RCS2019-56 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In a mesh network using IEEE 802.11 wireless LAN, each node can use multiple transmission rates defined by the combination of modulation schemes and coding rates. By selecting the transmission rate according to the communication environment, the throughput can be maximized. The conventional algorithm aims at maximizing throughput by referring to Received Signal Strength Indicator (RSSI), a transmission success rate, and an acknowledgment frame. However, realizing maximum throughput is difficult because of various factors such as one-to-one correspondence between RSSI and throughput, unnecessary transmission rate control due to a hidden terminal problem, and the ability to acquire more than one RSSI, for multiple antennas. In this study, we treat the RSSI and transmission rate collected by experiment as a data set, and use machine learning to maximize throughput by selecting the optimal transmission rate for each node. Training is carried out by using support vector machine (SVM) and k-neighbor method (kNN) about the created data set. From a result of testing, it is reported that the value of 83.2％ of the optimum value of throughput can be obtained at the maximum. |
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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. 119, no. 90, RCS2019-56, pp. 113-117, June 2019. | |
| Paper # | RCS2019-56 | |
| Date of Issue | 2019-06-12 (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 | RCS2019-56 |
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| Conference Information | |
| Committee | RCS |
| Conference Date | 2019-06-19 - 2019-06-21 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Miyakojima Hirara Port Terminal Building |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | First Presentation in IEICE Technical Committee, Resource Control, Scheduling, Wireless Communications, etc. |
| Paper Information | |
| Registration To | RCS |
| Conference Code | 2019-06-RCS |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Study on Application of Machine Learning to Transmission Rate Selection in Wireless Mesh Networks |
| 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 | Soukiw 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 | Kentaro Kobayashi |
| 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 | 2019-06-19 15:35:00 |
| Presentation Time | 10 minutes |
| Registration for | RCS |
| Paper # | RCS2019-56 |
| Volume (vol) | vol.119 |
| Number (no) | no.90 |
| Page | pp.113-117 |
| #Pages | 5 |
| Date of Issue | 2019-06-12 (RCS) |
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