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Paper Abstract and Keywords
Presentation 2022-01-21 15:00
[Invited Talk] Deep Learning-Aided Belief Propagation for Large Multiuser MIMO Detection
Takumi Takahashi (Osaka Univ.), Shinsuke Ibi (Doshisha Univ.), Seiichi Sampei (Osaka Univ.) IT2021-80 SIP2021-88 RCS2021-248
Abstract (in Japanese) (See Japanese page) 
(in English) With the increasing dimensionality of wireless communication signals, low-complexity signal detection algorithms to solve large-scale linear inference problems are expected to be crucial in future wireless networks. As a low-complexity linear Bayesian detector, Gaussian belief propagation (GaBP) achieves a Bayes-optimal performance with the minimum computational complexity in the large-system limit when the observation matrix entries follow an independent and identically distributed (i.i.d.) Gaussian distribution with zero mean. However, it is difficult to make such an ideal condition hold true for linear inference problems encountered in signal processing for wireless communications, and the optimal performance is not achieved even by employing sophisticated BP-based algorithms. As a promising approach to bridge this gap between theory and engineering, deep unfolding (DU), which embeds parameters into existing iterative algorithms and optimizes them via data-driven tuning, has been gaining attention. As an example, this paper will show that the DU technique can mitigate the inconvenience caused by the difference between the actual wireless communication environments and the ideal condition when GaBP is used for uplink signal detection in large multi-user MIMO (MU-MIMO) systems. The above example is employed to describe the design of trainable algorithms, the interpretation of the algorithms after training, and the performance evaluation, and we provide the essence of integrating BP and deep learning (DL) to design a practical signal detection algorithm.
Keyword (in Japanese) (See Japanese page) 
(in English) belief propagation / deep learning / large MIMO detection / deep unfolding / data-driven tuning / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 327, IT2021-80, pp. 289-294, Jan. 2022.
Paper # IT2021-80 
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 IT2021-80 SIP2021-88 RCS2021-248

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 IT 
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) Deep Learning-Aided Belief Propagation for Large Multiuser MIMO Detection 
Sub Title (in English)  
Keyword(1) belief propagation  
Keyword(2) deep learning  
Keyword(3) large MIMO detection  
Keyword(4) deep unfolding  
Keyword(5) data-driven tuning  
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1st Author's Name Takumi Takahashi  
1st Author's Affiliation Osaka University (Osaka Univ.)
2nd Author's Name Shinsuke Ibi  
2nd Author's Affiliation Doshisha University (Doshisha Univ.)
3rd Author's Name Seiichi Sampei  
3rd Author's Affiliation Osaka University (Osaka Univ.)
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Speaker Author-1 
Date Time 2022-01-21 15:00:00 
Presentation Time 50 minutes 
Registration for IT 
Paper # IT2021-80, SIP2021-88, RCS2021-248 
Volume (vol) vol.121 
Number (no) no.327(IT), no.328(SIP), no.329(RCS) 
Page pp.289-294 
#Pages 6 
Date of Issue 2022-01-13 (IT, SIP, RCS) 

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