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| Presentation | 2019-07-11 10:40 [Poster Presentation] Blind SIR Estimation by Deep Learning Using Visualized Wireless Signal Information Kazuki Maruta, Shun Kojima (Chiba Univ.), Yu Nakayama (TUAT), Daisuke Hisano (Osaka Univ.), Chang-Jun Ahn (Chiba Univ.)  RCC2019-27 NS2019-63 RCS2019-120 SR2019-39 SeMI2019-36 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | This article proposes the blind interference estimation via deep learning approach exploiting the visualized wireless signal information. Co-channel interference becomes more extensive due to frequency resource exhaustion and small cell deployment which had been triggered by mobile traffic explosion. Multi-antenna signal processing, i.e. blind adaptive array, is an effective means to suppress co-channel interference without any a priori information such as channel state information. Unfortunately, blind algorithms have their applicable regions depending on the signal-to-interference (SIR) at array input. These algorithms should be optimally selected according to interference level. Here investigates the possibility of the SIR classification by the multi-layered deep convolutional neural network. Constellation images where includes the desired and interference signals are used for model training. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Machine learning / Deep learning / Neural network / Interference estimation / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 108, RCS2019-120, pp. 107-108, July 2019. | |
| Paper # | RCS2019-120 | |
| Date of Issue | 2019-07-03 (RCC, NS, RCS, SR, SeMI) | |
| 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 | RCC2019-27 NS2019-63 RCS2019-120 SR2019-39 SeMI2019-36 |
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| Conference Information | |
| Committee | SeMI RCS NS SR RCC |
| Conference Date | 2019-07-10 - 2019-07-12 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | I-Site Nanba（Osaka） |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Communication and Networked Control for the Future Radio of the AI Age, etc |
| Paper Information | |
| Registration To | RCS |
| Conference Code | 2019-07-SeMI-RCS-NS-SR-RCC |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Blind SIR Estimation by Deep Learning Using Visualized Wireless Signal Information |
| Sub Title (in English) | |
| Keyword(1) | Machine learning |
| Keyword(2) | Deep learning |
| Keyword(3) | Neural network |
| Keyword(4) | Interference estimation |
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| 1st Author's Name | Kazuki Maruta |
| 1st Author's Affiliation | Chiba University (Chiba Univ.) |
| 2nd Author's Name | Shun Kojima |
| 2nd Author's Affiliation | Chiba University (Chiba Univ.) |
| 3rd Author's Name | Yu Nakayama |
| 3rd Author's Affiliation | Tokyo University of Agriculture and Technology (TUAT) |
| 4th Author's Name | Daisuke Hisano |
| 4th Author's Affiliation | Osaka University (Osaka Univ.) |
| 5th Author's Name | Chang-Jun Ahn |
| 5th Author's Affiliation | Chiba University (Chiba Univ.) |
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| Speaker | Author-1 |
| Date Time | 2019-07-11 10:40:00 |
| Presentation Time | 80 minutes |
| Registration for | RCS |
| Paper # | RCC2019-27, NS2019-63, RCS2019-120, SR2019-39, SeMI2019-36 |
| Volume (vol) | vol.119 |
| Number (no) | no.106(RCC), no.107(NS), no.108(RCS), no.109(SR), no.110(SeMI) |
| Page | pp.85-86(RCC), pp.111-112(NS), pp.107-108(RCS), pp.117-118(SR), pp.99-100(SeMI) |
| #Pages | 2 |
| Date of Issue | 2019-07-03 (RCC, NS, RCS, SR, SeMI) |
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