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Paper Abstract and Keywords
Presentation 2025-01-29 17:00
A Study on Model-Based Deep Learning of EP-Aided Turbo Equalization for Overloaded MIMO Detection
Haruki Sekiguchi, Shinsuke Ibi (Doshisha Univ.), Takumi Takahashi (Osaka Univ.), Hisato Iwai (Doshisha Univ.) IT2024-43 SIP2024-82 RCS2024-226
Abstract (in Japanese) (See Japanese page) 
(in English) This paper proposes a model-based deep learning for the turbo equalizer (TuE) with an iterative demodulation and detection (IDD) structure, which exchanges the log-likelihood ratio (LLR) between the expectation propagation (EP) detector and the channel decoder, in overloaded multiple-input multiple-output (MIMO) detection. Since overload MIMO signal detection is an under-determined problem, the typical TuE, which uses a linear minimum mean square error (MMSE) filter for signal detection, cannot sufficiently suppress interference.To address this issue, we introduce the EP-based iterative detection into TuE instead of the linear MMSE filter.However, even when the EP detector is introduced, it is difficult to suppress the effects of self-feedback and exchange ideal extrinsic values in medium-sized system configurations.To solve this problem, we introduce adaptive belief scaling and belief damping to the EP detector, and further improve the detection accuracy by optimizing the parameters controlling these methods through model-based deep learning.Finally, computer simulations demonstrate the validity of the proposed method in terms of bit error rate (BER) performance.
Keyword (in Japanese) (See Japanese page) 
(in English) Overloaded MIMO / expectation propagation / turbo equalization / scaling / damping / model-based deep learning / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 366, RCS2024-226, pp. 98-103, Jan. 2025.
Paper # RCS2024-226 
Date of Issue 2025-01-22 (IT, SIP, 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 IT2024-43 SIP2024-82 RCS2024-226

Conference Information
Committee RCS SIP IT  
Conference Date 2025-01-29 - 2025-01-30 
Place (in Japanese) (See Japanese page) 
Place (in English) Kaikyou-messe 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Signal Processing for Wireless Communications, Learning, Mathematics, Information Theory, etc. 
Paper Information
Registration To RCS 
Conference Code 2025-01-RCS-SIP-IT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study on Model-Based Deep Learning of EP-Aided Turbo Equalization for Overloaded MIMO Detection 
Sub Title (in English)  
Keyword(1) Overloaded MIMO  
Keyword(2) expectation propagation  
Keyword(3) turbo equalization  
Keyword(4) scaling  
Keyword(5) damping  
Keyword(6) model-based deep learning  
Keyword(7)  
Keyword(8)  
1st Author's Name Haruki Sekiguchi  
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 Hisato Iwai  
4th Author's Affiliation Doshisha University (Doshisha Univ.)
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Speaker Author-1 
Date Time 2025-01-29 17:00:00 
Presentation Time 25 minutes 
Registration for RCS 
Paper # IT2024-43, SIP2024-82, RCS2024-226 
Volume (vol) vol.124 
Number (no) no.364(IT), no.365(SIP), no.366(RCS) 
Page pp.98-103 
#Pages
Date of Issue 2025-01-22 (IT, SIP, RCS) 


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