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Paper Abstract and Keywords
Presentation 2026-06-18 11:05
A Study on Wireless Autoencoder for Independent Component Analysis-Aided Signal Detection
Eita Kino, Shinsuke Ibi, Hisato Iwai (Doshisya Univ.) RCS2026-56
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, the growth in communication traffic has increased the demand for wireless technologies with higher capacity and data rates. A promising solution is multi-input multi-output (MIMO) communication, which enables spatial multiplexing at the same time and frequency. In general, spatially multiplexed signals are separated by estimating channel state information (CSI) from pilot signals and applying spatial filtering based on the estimate. However, long pilot sequences reduce the efficiency of short data packets. In such cases, independent component analysis (ICA), a blind signal separation (BSS) technique, can be used. However, because ICA involves nonlinear operations, an equally spaced constellation is not always optimal, and it is difficult to design the optimal constellation analytically. Moreover, for short data sequences, the candidate transmit vectors occur with biased probabilities, hindering accurate evaluation of non-Gaussianity and degradingBSS performance. Although parameterized iterations and damping have been studied, analytically deriving their optimal values remains difficult. In this paper, computer simulations demonstrate that model-based deep learning with a wireless autoencoder (WAE) improves ICA-based signal detection for short data sequences.
Keyword (in Japanese) (See Japanese page) 
(in English) ICA / MIMO / Blind Signal Separation / Wireless Autoencoder / Neural Network / Deep Unfolding / /  
Reference Info. IEICE Tech. Rep., vol. 126, no. 72, RCS2026-56, pp. 176-181, June 2026.
Paper # RCS2026-56 
Date of Issue 2026-06-10 (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 RCS2026-56

Conference Information
Committee RCS  
Conference Date 2026-06-17 - 2026-06-19 
Place (in Japanese) (See Japanese page) 
Place (in English) The Ohama Nobumoto Memorial Hall 
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 2026-06-RCS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study on Wireless Autoencoder for Independent Component Analysis-Aided Signal Detection 
Sub Title (in English)  
Keyword(1) ICA  
Keyword(2) MIMO  
Keyword(3) Blind Signal Separation  
Keyword(4) Wireless Autoencoder  
Keyword(5) Neural Network  
Keyword(6) Deep Unfolding  
Keyword(7)  
Keyword(8)  
1st Author's Name Eita Kino  
1st Author's Affiliation Doshisha University (Doshisya Univ.)
2nd Author's Name Shinsuke Ibi  
2nd Author's Affiliation Doshisha University (Doshisya Univ.)
3rd Author's Name Hisato Iwai  
3rd Author's Affiliation Doshisha University (Doshisya Univ.)
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Speaker Author-1 
Date Time 2026-06-18 11:05:00 
Presentation Time 10 minutes 
Registration for RCS 
Paper # RCS2026-56 
Volume (vol) vol.126 
Number (no) no.72 
Page pp.176-181 
#Pages 6 
Date of Issue 2026-06-10 (RCS) 

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