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Paper Abstract and Keywords
Presentation 2023-01-25 10:25
A Fundamental Study on Decoding Short Length Polar Codes by Deep Learning
Reona Kumaki, Hiroshi Tsutsui, Takeo Ohgane (Hokkaido Univ.) IT2022-52 SIP2022-103 RCS2022-231
Abstract (in Japanese) (See Japanese page) 
(in English) LDPC codes, Turbo codes, and polar codes are currently known
as the best channel codes achieving near Shannon limit.
They basically require long code lengths to exploit their full potential.
However, the performance for the shorter code length becomes an important issue
when we consider small-data communications such as IoT transmitting very few data
and control channels in cellular systems.
In this study, we apply deep learning to polar code decoding and
investigate fundamental characteristics for aiming to improve the coding gain
in the shorter code length case with reasonable calculation complexity.
Our simulation results in the AWGN environment show that even a neural network
with a relatively simple structure gives better BER performance
than successive-cancellation decoding which is commonly known.
In this report, to improve the error correction performance and
reduce processing time in such applications, we use deep learning to
construct decoders for (8, 4) polar codes.
Keyword (in Japanese) (See Japanese page) 
(in English) 5G / polar codes / deep neural network / small-data communications / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 357, RCS2022-231, pp. 132-135, Jan. 2023.
Paper # RCS2022-231 
Date of Issue 2023-01-17 (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 IT2022-52 SIP2022-103 RCS2022-231

Conference Information
Committee IT RCS SIP  
Conference Date 2023-01-24 - 2023-01-25 
Place (in Japanese) (See Japanese page) 
Place (in English) Maebashi Terrsa 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To RCS 
Conference Code 2023-01-IT-RCS-SIP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Fundamental Study on Decoding Short Length Polar Codes by Deep Learning 
Sub Title (in English)  
Keyword(1) 5G  
Keyword(2) polar codes  
Keyword(3) deep neural network  
Keyword(4) small-data communications  
Keyword(5)  
Keyword(6)  
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Keyword(8)  
1st Author's Name Reona Kumaki  
1st Author's Affiliation Hokkaido University (Hokkaido Univ.)
2nd Author's Name Hiroshi Tsutsui  
2nd Author's Affiliation Hokkaido University (Hokkaido Univ.)
3rd Author's Name Takeo Ohgane  
3rd Author's Affiliation Hokkaido University (Hokkaido Univ.)
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Speaker Author-1 
Date Time 2023-01-25 10:25:00 
Presentation Time 25 minutes 
Registration for RCS 
Paper # IT2022-52, SIP2022-103, RCS2022-231 
Volume (vol) vol.122 
Number (no) no.355(IT), no.356(SIP), no.357(RCS) 
Page pp.132-135 
#Pages 4 
Date of Issue 2023-01-17 (IT, SIP, RCS) 

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