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Paper Abstract and Keywords
Presentation 2021-06-23 09:50
A basic study on signal detection using learned approximate message passing
Mari Miyoshi, Wakaba Tsujimoto, Toshihiko Nishimura, Takeo Ohgane, Yasutaka Ogawa, Junichiro Hagiwara, Takanori Sato (Hokkaido Univ.) RCS2021-31
Abstract (in Japanese) (See Japanese page) 
(in English) Approximate message passing (AMP) is applicable to massive MIMO signal detection and achieves a high detection performance with low computational complexity. However, when two conditions required by AMP, i.e., the large system limit and a property that each entry of the channel matrix follows an independent and identically distributed complex Gaussian distribution, are not satisfied, the detection performance is severely degraded. It has been found that the degradation is relaxed by introducing a constant multiplier to the observation rate which is the ratio of the numbers of received to transmitted signals. The optimal value of the multiplier depends on the numbers of transmit and receive antennas, signal-to-noise ratio, and other conditions. In this paper, we replace two terms including the observation rate with independent parameters and optimize them by deep unfolding as a learned AMP. Simulation results show that the degradation is highly reduced by the optimized network and that the network is applicable to various channel conditions.
Keyword (in Japanese) (See Japanese page) 
(in English) MIMO / approximate message passing / deep learning / deep unfolding / spatial correlation / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 72, RCS2021-31, pp. 13-18, June 2021.
Paper # RCS2021-31 
Date of Issue 2021-06-16 (RCS) 
ISSN Online edition: ISSN 2432-6380
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 RCS2021-31

Conference Information
Committee RCS  
Conference Date 2021-06-23 - 2021-06-25 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
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 2021-06-RCS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A basic study on signal detection using learned approximate message passing 
Sub Title (in English)  
Keyword(1) MIMO  
Keyword(2) approximate message passing  
Keyword(3) deep learning  
Keyword(4) deep unfolding  
Keyword(5) spatial correlation  
1st Author's Name Mari Miyoshi  
1st Author's Affiliation Hokkaido University (Hokkaido Univ.)
2nd Author's Name Wakaba Tsujimoto  
2nd Author's Affiliation Hokkaido University (Hokkaido Univ.)
3rd Author's Name Toshihiko Nishimura  
3rd Author's Affiliation Hokkaido University (Hokkaido Univ.)
4th Author's Name Takeo Ohgane  
4th Author's Affiliation Hokkaido University (Hokkaido Univ.)
5th Author's Name Yasutaka Ogawa  
5th Author's Affiliation Hokkaido University (Hokkaido Univ.)
6th Author's Name Junichiro Hagiwara  
6th Author's Affiliation Hokkaido University (Hokkaido Univ.)
7th Author's Name Takanori Sato  
7th Author's Affiliation Hokkaido University (Hokkaido Univ.)
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Speaker Author-1 
Date Time 2021-06-23 09:50:00 
Presentation Time 10 minutes 
Registration for RCS 
Paper # RCS2021-31 
Volume (vol) vol.121 
Number (no) no.72 
Page pp.13-18 
Date of Issue 2021-06-16 (RCS) 

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