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| Presentation | 2018-02-27 14:15 Coverage Expansion in mmWave V2I Communications by Deep Reinforcement Learning Based Vehicle Re-deployment Akihito Taya, Takayuki Nishio, Masahiro Morikura, Koji Yamamoto (Kyoto Univ.)  MoNA2017-69 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | The small coverage of road side units (RSUs) is one of the challenges in millimeter wave (mmWave) communications for autonomous vehicles. A key concept of this paper is leveraging vehicles’ position controllability to increase the coverages of RSUs by creating long multi-hop relays. Vehicles should be positioned where they can communicate with each other in line-of-sight (LOS) paths because the blockages of LOS paths is a crucial problem in mmWave communications. In this paper, a vehicle position control method based on reinforcement learning (RL) is proposed to avoid blockage and create long relays in order to increase coverage. RL enables vehicles to predict the future coverages achieved by their subsequent actions and therefore, its performance exceeds that of a conventional strategy which is used for moving control of wireless sensor networks. This paper also proposes state designs with information of relay length to improve the performance of increasing coverages. Simulation results confirm that the proposed method increase coverages even when not all the vehicles’ positions are controllable. In addition, it is shown that a RL-based strategy achieves higher performances than gradient-based strategies. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Vehicular Communications / MmWave Communications / Multihop / Deep Reinforcement Learning / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 117, no. 450, MoNA2017-69, pp. 317-322, Feb. 2018. | |
| Paper # | MoNA2017-69 | |
| Date of Issue | 2018-02-19 (MoNA) | |
| ISSN | Print edition: ISSN 0913-5685 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 | MoNA2017-69 |
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| Conference Information | |
| Committee | MoNA ASN IPSJ-MBL IPSJ-UBI |
| Conference Date | 2018-02-26 - 2018-02-27 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Sophia University |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Ubiquitous system, Ambient intelligence, Next generation wireless communicasion, Mobile network, etc. |
| Paper Information | |
| Registration To | MoNA |
| Conference Code | 2018-02-MoNA-ASN-MBL-UBI |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Coverage Expansion in mmWave V2I Communications by Deep Reinforcement Learning Based Vehicle Re-deployment |
| Sub Title (in English) | |
| Keyword(1) | Vehicular Communications |
| Keyword(2) | MmWave Communications |
| Keyword(3) | Multihop |
| Keyword(4) | Deep Reinforcement Learning |
| Keyword(5) | |
| Keyword(6) | |
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| 1st Author's Name | Akihito Taya |
| 1st Author's Affiliation | Kyoto University (Kyoto Univ.) |
| 2nd Author's Name | Takayuki Nishio |
| 2nd Author's Affiliation | Kyoto University (Kyoto Univ.) |
| 3rd Author's Name | Masahiro Morikura |
| 3rd Author's Affiliation | Kyoto University (Kyoto Univ.) |
| 4th Author's Name | Koji Yamamoto |
| 4th Author's Affiliation | Kyoto University (Kyoto Univ.) |
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| Speaker | Author-1 |
| Date Time | 2018-02-27 14:15:00 |
| Presentation Time | 25 minutes |
| Registration for | MoNA |
| Paper # | MoNA2017-69 |
| Volume (vol) | vol.117 |
| Number (no) | no.450 |
| Page | pp.317-322 |
| #Pages | 6 |
| Date of Issue | 2018-02-19 (MoNA) |
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