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| Presentation | 2018-05-24 15:05 A Study of Proactive Beam Forming Control using Machine Learning for 5G Mobile Communication System Takashi Seyama, Teppei Oyama, Takashi Dateki (Fujitsu Lab.)  SR2018-7 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In fifth generation mobile communication systems, beam forming have been studied in order to compensate increase of pathloss at higher frequency than 4G. In beam forming at high frequency, the received power will degrade when UE moves behind buildings or traffic signs or a human bodies. In this paper, we proposed a proactive beam forming control on the basis of prediction of received power change using machine learning techniques to mitigate blockage effect by static objects when UE moves. As a preliminary study, we study prediction of the received power degradation event by means of neural network whose inputs are time series data of beam index and received power fed back by UE. We train the prediction model using training data generated by computer simulations and evaluate the success rate, the miss detection rate and the false alarm rate. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | 5G / Beam forming / Proactive beam forming control / Machine Learning / Neural Network / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 118, no. 57, SR2018-7, pp. 43-48, May 2018. | |
| Paper # | SR2018-7 | |
| Date of Issue | 2018-05-17 (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 | SR2018-7 |
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| Conference Information | |
| Committee | SR |
| Conference Date | 2018-05-24 - 2018-05-25 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Tokyo big sight |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Technical Exhibition, Machine Learning, AI |
| Paper Information | |
| Registration To | SR |
| Conference Code | 2018-05-SR |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Study of Proactive Beam Forming Control using Machine Learning for 5G Mobile Communication System |
| Sub Title (in English) | |
| Keyword(1) | 5G |
| Keyword(2) | Beam forming |
| Keyword(3) | Proactive beam forming control |
| Keyword(4) | Machine Learning |
| Keyword(5) | Neural Network |
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| Keyword(8) | |
| 1st Author's Name | Takashi Seyama |
| 1st Author's Affiliation | FUJITSU LABORATORIES LTD. (Fujitsu Lab.) |
| 2nd Author's Name | Teppei Oyama |
| 2nd Author's Affiliation | FUJITSU LABORATORIES LTD. (Fujitsu Lab.) |
| 3rd Author's Name | Takashi Dateki |
| 3rd Author's Affiliation | FUJITSU LABORATORIES LTD. (Fujitsu Lab.) |
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| Speaker | Author-1 |
| Date Time | 2018-05-24 15:05:00 |
| Presentation Time | 25 minutes |
| Registration for | SR |
| Paper # | SR2018-7 |
| Volume (vol) | vol.118 |
| Number (no) | no.57 |
| Page | pp.43-48 |
| #Pages | 6 |
| Date of Issue | 2018-05-17 (SR) |
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