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) |
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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) |
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Keyword(1) |
Overloaded MIMO |
Keyword(2) |
expectation propagation |
Keyword(3) |
turbo equalization |
Keyword(4) |
scaling |
Keyword(5) |
damping |
Keyword(6) |
model-based deep learning |
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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 |
6 |
Date of Issue |
2025-01-22 (IT, SIP, RCS) |