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Paper Abstract and Keywords
Presentation 2022-06-16 14:55
A study on model parameters for MIMO signal detection using learned AMP
Mari Miyoshi, Toshihiko Nishimura, Takanori Sato, Takeo Ohgane, Yasutaka Ogawa, Junichiro Hagiwara (Hokkaido Univ.) RCS2022-50
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. Learned AMP (LAMP) , which is based on deep unfolding, can perform signal detection while optimizing the multiplier, and has high detection performance. In this paper, we compare the model parameters used for learning. It is found that modification to residual interference power is needed for proper learning when model parameters based on the strict AMP algorithm are used.
Keyword (in Japanese) (See Japanese page) 
(in English) MIMO / approximate message passing / deep learning / deep unfolding / spatial correlation / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 73, RCS2022-50, pp. 156-161, June 2022.
Paper # RCS2022-50 
Date of Issue 2022-06-08 (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 RCS2022-50

Conference Information
Committee RCS  
Conference Date 2022-06-15 - 2022-06-17 
Place (in Japanese) (See Japanese page) 
Place (in English) University of the Ryukyus, Senbaru Campus and 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 2022-06-RCS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A study on model parameters for MIMO signal detection using learned AMP 
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 Toshihiko Nishimura  
2nd Author's Affiliation Hokkaido University (Hokkaido Univ.)
3rd Author's Name Takanori Sato  
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.)
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Speaker Author-1 
Date Time 2022-06-16 14:55:00 
Presentation Time 25 minutes 
Registration for RCS 
Paper # RCS2022-50 
Volume (vol) vol.122 
Number (no) no.73 
Page pp.156-161 
Date of Issue 2022-06-08 (RCS) 

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