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Paper Abstract and Keywords
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
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

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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