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Paper Abstract and Keywords
Presentation 2023-01-25 12:20
A Study on Wireless Autoencoder for Data-Aided Channel Estimation
Yoshinori Ichihashi, Shinsuke Ibi (Doshisha Univ.), Takumi Takahashi (Osaka Univ.), Takanobu Doi, Kazushi Muraoka, Naoto Ishii (NEC), Hisato Iwai (Doshisha Univ.) IT2022-56 SIP2022-107 RCS2022-235
Abstract (in Japanese) (See Japanese page) 
(in English) In MIMO (Multiple-Input Multiple-Output) channels, when the channel matrix is obtained by the Data-Aided Channel Estimation, and then the maximum likelihood detection (MLD) is performed based on the estimated matrix, the noise covariance matrix becomes no longer a diagonal matrix, and the noise impairs the whiteness.
In signal detection under white Gaussian noise, geometric signal constellation design, which maximizes the minimum Euclidean distance, is the optimal solution in terms of minimizing the symbol error rate (SER).
However, it is not necessarily optimal for non-white Gaussian channels.
When it is analytically difficult to design the optimal signal constellation, a data-driven optimization method using a wireless autoencoder (WAE) is effective, which has a neural network for generating the signal constellation and minimizes the cross entropy between the transmission symbols and the likelihoods obtained through MLDs.
In this paper, we perform data-driven tuning of the wireless autoencoder for iterative and successive channel estimations following data-aided approaches.
Finally, the validity of the tuned WAE is confirmed by computer simulations.
Keyword (in Japanese) (See Japanese page) 
(in English) Iterative channel estimation / signal constellation / autoencoder / data-driven tuning / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 357, RCS2022-235, pp. 154-159, Jan. 2023.
Paper # RCS2022-235 
Date of Issue 2023-01-17 (IT, SIP, RCS) 
ISSN Online edition: ISSN 2432-6380
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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 IT2022-56 SIP2022-107 RCS2022-235

Conference Information
Committee IT RCS SIP  
Conference Date 2023-01-24 - 2023-01-25 
Place (in Japanese) (See Japanese page) 
Place (in English) Maebashi Terrsa 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To RCS 
Conference Code 2023-01-IT-RCS-SIP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study on Wireless Autoencoder for Data-Aided Channel Estimation 
Sub Title (in English)  
Keyword(1) Iterative channel estimation  
Keyword(2) signal constellation  
Keyword(3) autoencoder  
Keyword(4) data-driven tuning  
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1st Author's Name Yoshinori Ichihashi  
1st Author's Affiliation Doshisha University (Doshisha Univ.)
2nd Author's Name Shinsuke Ibi  
2nd Author's Affiliation Doshisha University (Doshisha Univ.)
3rd Author's Name Takumi Takahashi  
3rd Author's Affiliation Osaka University (Osaka Univ.)
4th Author's Name Takanobu Doi  
4th Author's Affiliation NEC Corporation (NEC)
5th Author's Name Kazushi Muraoka  
5th Author's Affiliation NEC Corporation (NEC)
6th Author's Name Naoto Ishii  
6th Author's Affiliation NEC Corporation (NEC)
7th Author's Name Hisato Iwai  
7th Author's Affiliation Doshisha University (Doshisha Univ.)
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Speaker Author-1 
Date Time 2023-01-25 12:20:00 
Presentation Time 25 minutes 
Registration for RCS 
Paper # IT2022-56, SIP2022-107, RCS2022-235 
Volume (vol) vol.122 
Number (no) no.355(IT), no.356(SIP), no.357(RCS) 
Page pp.154-159 
#Pages
Date of Issue 2023-01-17 (IT, SIP, RCS) 


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