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Paper Abstract and Keywords
Presentation 2022-01-21 10:55
A Study on Bayesian Receiver Design via Deep Unfolding-Aided Bilinear Inference for Correlated Large MIMO
Ryota Tamaki, Kenta Ito, Takumi Takahashi (Osaka Univ.), Shinsuke Ibi (Doshisha Univ.), Seiichi Sampei (Osaka Univ.) IT2021-65 SIP2021-73 RCS2021-233
Abstract (in Japanese) (See Japanese page) 
(in English) This paper proposes a joint channel and data estimation (JCDE) scheme via deep unfolding (DU)-aided bilinear generalized approximate message passing (BiGAMP) for large multi-user multi-input multi-output (MU-MIMO) systems. BiGAMP is one of the promising JCDE schemes to achieve highly accurate multi-user detection (MUD) with low computational cost, which separates high dimensional signals based on matched filters (MFs). However, it is designed based on the large systems assuming independent and identically distributed (i.i.d.) observations with mean zero; therefore, the estimation capability is severely degraded in the presence of spatial fading correlation. To tackle this issue, we design a novel trainable BiGAMP (T-BiGAMP) that incorporates trainable internal parameters into the iterative process of the JCDE based on BiGAMP. By optimizing the parameters via textit{data-driven} tuning techniques, the JCDE algorithm based on T-BiGAMP achieves high estimation performance even in practical MIMO configurations that differ from the ideal operating conditions. Computer simulations demonstrate the validity of our proposed method in terms of bit error rate (BER) performance.
Keyword (in Japanese) (See Japanese page) 
(in English) Correlated large MIMO detection / deep unfolding / BiGAMP / data-drive tuning / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 329, RCS2021-233, pp. 207-212, Jan. 2022.
Paper # RCS2021-233 
Date of Issue 2022-01-13 (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 IT2021-65 SIP2021-73 RCS2021-233

Conference Information
Committee RCS SIP IT  
Conference Date 2022-01-20 - 2022-01-21 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To RCS 
Conference Code 2022-01-RCS-SIP-IT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study on Bayesian Receiver Design via Deep Unfolding-Aided Bilinear Inference for Correlated Large MIMO 
Sub Title (in English)  
Keyword(1) Correlated large MIMO detection  
Keyword(2) deep unfolding  
Keyword(3) BiGAMP  
Keyword(4) data-drive tuning  
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1st Author's Name Ryota Tamaki  
1st Author's Affiliation Osaka University (Osaka Univ.)
2nd Author's Name Kenta Ito  
2nd Author's Affiliation Osaka University (Osaka Univ.)
3rd Author's Name Takumi Takahashi  
3rd Author's Affiliation Osaka University (Osaka Univ.)
4th Author's Name Shinsuke Ibi  
4th Author's Affiliation Doshisha University (Doshisha Univ.)
5th Author's Name Seiichi Sampei  
5th Author's Affiliation Osaka University (Osaka Univ.)
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Speaker Author-1 
Date Time 2022-01-21 10:55:00 
Presentation Time 25 minutes 
Registration for RCS 
Paper # IT2021-65, SIP2021-73, RCS2021-233 
Volume (vol) vol.121 
Number (no) no.327(IT), no.328(SIP), no.329(RCS) 
Page pp.207-212 
#Pages 6 
Date of Issue 2022-01-13 (IT, SIP, RCS) 

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