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Paper Abstract and Keywords
Presentation 2019-06-19 10:30
A Fundamental Study on Channel Estimation Using Deep Learning
Ryota Nakajima, Toshihiko Nishimura, Takeo Ohgane, Yasutaka Ogawa, Junichiro Hagiwara (Hokkaido Univ.) RCS2019-43
Abstract (in Japanese) (See Japanese page) 
(in English) In wireless communication, channel estimation is essential to detect transmitted signals from received signals. At present, the channel estimation is generally performed using a orthogonal pilot sequence such as Zadoff-Chu sequence, which has a constant amplitude and an impulsive autocorrelation function, i.e., a constant power spectral density in the frequency domain. In some cases such as large MIMO systems,
however, the number of pilot signals may be limited to maintain the transmission efficiency, and there may be a shortage of pilot signals. Non-orthogonal pilot sequences can be generated easily without a limitation on the number of sequences although it causes some estimation error. In this paper, we apply deep learning to reduce the estimation error caused by non-orthogonality among the pilots and evaluate how it works. In this paper, numerical evaluation results assuming Gold sequences as a non-orthogonal sequence and generative adversarial networks as a deep learning model show that we can improve the degradation of channel estimation accuracy to some extent.
Keyword (in Japanese) (See Japanese page) 
(in English) Channel estimation / non-orthogonal pilot / deep learning / generative adversarial networks / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 90, RCS2019-43, pp. 37-42, June 2019.
Paper # RCS2019-43 
Date of Issue 2019-06-12 (RCS) 
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 RCS2019-43

Conference Information
Committee RCS  
Conference Date 2019-06-19 - 2019-06-21 
Place (in Japanese) (See Japanese page) 
Place (in English) Miyakojima Hirara Port Terminal Building 
Topics (in Japanese) (See Japanese page) 
Topics (in English) First Presentation in IEICE Technical Committee, Resource Control, Scheduling, Wireless Communications, etc. 
Paper Information
Registration To RCS 
Conference Code 2019-06-RCS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Fundamental Study on Channel Estimation Using Deep Learning 
Sub Title (in English)  
Keyword(1) Channel estimation  
Keyword(2) non-orthogonal pilot  
Keyword(3) deep learning  
Keyword(4) generative adversarial networks  
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1st Author's Name Ryota Nakajima  
1st Author's Affiliation Hokkaido University (Hokkaido Univ.)
2nd Author's Name Toshihiko Nishimura  
2nd Author's Affiliation Hokkaido University (Hokkaido Univ.)
3rd Author's Name Takeo Ohgane  
3rd Author's Affiliation Hokkaido University (Hokkaido Univ.)
4th Author's Name Yasutaka Ogawa  
4th Author's Affiliation Hokkaido University (Hokkaido Univ.)
5th Author's Name Junichiro Hagiwara  
5th Author's Affiliation Hokkaido University (Hokkaido Univ.)
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Speaker Author-1 
Date Time 2019-06-19 10:30:00 
Presentation Time 10 minutes 
Registration for RCS 
Paper # RCS2019-43 
Volume (vol) vol.119 
Number (no) no.90 
Page pp.37-42 
#Pages 6 
Date of Issue 2019-06-12 (RCS) 

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