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Paper Abstract and Keywords
Presentation 2021-03-04 11:35
[Poster Presentation] A Study on Fading Variation Estimation Employing Deep Learning Based on Level Crossing Rate
Koshiro Kawachi, Yukiko Shimbo, Hirofumi Suganuma, Fumiaki Maehara (Waseda Univ.) RCS2020-233 SR2020-72 SRW2020-62
Abstract (in Japanese) (See Japanese page) 
(in English) Fifth-generation (5G) mobile communication systems entail various scenarios such as enhanced mobile broadband (eMBB), ultra-reliable and low-latency communications (URLLC), and massive machine type communications (mMTC). In order to realize various requirements of 5G, it is expected to easily estimate channel conditions such as time selectivity which affects transmission performance. So far, we have proposed a time selectivity estimation method using level crossing rate, which estimates Doppler frequency just only by counting level cross of channel variation. As an extension of this work, we propose a deep-learning-based fading variation estimation method using level crossing rate. The estimation performance of the proposed method is demonstrated in comparison with the traditional estimation method on the assumption of Rayleigh fading as a starting point for evaluation under different types of fading channels.
Keyword (in Japanese) (See Japanese page) 
(in English) level crossing rate / time selective fading / doppler frequency / deep learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 404, RCS2020-233, pp. 155-156, March 2021.
Paper # RCS2020-233 
Date of Issue 2021-02-24 (RCS, SR, SRW) 
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 RCS2020-233 SR2020-72 SRW2020-62

Conference Information
Committee RCS SR SRW  
Conference Date 2021-03-03 - 2021-03-05 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Mobile Communication Workshop 
Paper Information
Registration To RCS 
Conference Code 2021-03-RCS-SR-SRW 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study on Fading Variation Estimation Employing Deep Learning Based on Level Crossing Rate 
Sub Title (in English)  
Keyword(1) level crossing rate  
Keyword(2) time selective fading  
Keyword(3) doppler frequency  
Keyword(4) deep learning  
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1st Author's Name Koshiro Kawachi  
1st Author's Affiliation Waseda University (Waseda Univ.)
2nd Author's Name Yukiko Shimbo  
2nd Author's Affiliation Waseda University (Waseda Univ.)
3rd Author's Name Hirofumi Suganuma  
3rd Author's Affiliation Waseda University (Waseda Univ.)
4th Author's Name Fumiaki Maehara  
4th Author's Affiliation Waseda University (Waseda Univ.)
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Speaker Author-1 
Date Time 2021-03-04 11:35:00 
Presentation Time 40 minutes 
Registration for RCS 
Paper # RCS2020-233, SR2020-72, SRW2020-62 
Volume (vol) vol.120 
Number (no) no.404(RCS), no.405(SR), no.406(SRW) 
Page pp.155-156(RCS), pp.48-49(SR), pp.43-44(SRW) 
#Pages 2 
Date of Issue 2021-02-24 (RCS, SR, SRW) 

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