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Training Method of Deep Learning-Based Decoder for Punctured Polar Codes
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천공된 극 부호를 위한 딥 러닝 기반 복호기의 학습 방법

논문 기본 정보

Type
Academic journal
Author
Eun Young Seo (성균관대학교) Yeon Joon Choi (삼성전자) Jong-Hwan Kim (삼성전자) Sang-Hyo Kim (성균관대학교)
Journal
Korea Institute Of Communication Sciences The Journal of Korean Institute of Communications and Information Sciences Vol.43 No.7 KCI Accredited Journals SCOPUS
Published
2018.7
Pages
1,176 - 1,181 (6page)
DOI
10.7840/kics.2018.43.7.1176

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Training Method of Deep Learning-Based Decoder for Punctured Polar Codes
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Research history (4)

2019

Deep learning-based decoder for punctured polar codes
서은영 전자전기컴퓨터공학과 2019.01 Thesis

2018

Training Method of Deep Learning-Based Decoder for Punctured Polar Codes
Eun Young Seo ,  Yeon Joon Choi ,  Jong-Hwan Kim and others 1 The Journal of Korean Institute of Communications and Information Sciences 2018.07 Academic journal
A Training Method of Deep Learning-Based Polar Decoder for Punctured Polar Codes
Eun Young Seo ,  Yeon Joon Choi ,  Jong-Hwan Kim and others 1 Proceedings of Symposium of the Korean Institute of communications and Information Sciences 2018.01 Proceeding

2017

Deep Learning-Based Polar Decoder with Different Training Sets
Eun Young Seo ,  Yeon Joon Choi ,  Jong-Hwan Kim and others 1 대한전자공학회 학술대회 2017.11 Proceeding
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Abstract· Keywords

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Recently, various decoders with deep learning structures for linear codes have been proposed, and a decoder with feedforward neural network structure for polar codes has been shown to be near-optimal performance when sufficiently learned. However, in the previous research, performance was evaluated by only mother code without considering puncturing scheme for length-compatibility of polar codes. Therefore, in this paper, we show the performance of existing neural network decoder for punctured polar codes and propose a training method to efficiently learn punctured polar codes.
#deep learning #channel coding #polar codes #deep neural network(DNN) #puncturing scheme

Contents

요약
ABSTRACT
Ⅰ. 서론
Ⅱ. 심층 신경망 구조의 극 부호 복호기
Ⅲ. 천공 부호를 위한 신경망 복호기의 학습기법
Ⅳ. 결론
References

References (8)

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E. Nachmani, E. Marciano, L. Lugosch, Warren J. Gross, D. Burshtein, and Y. Be’ery, “Deep learning methods for improved decoding of linear codes,” IEEE J. Sel. Topics in Signal Process., vol. 12, no. 1, pp. 119-131, Feb. 2018. A. Bennatan, Y. Choukroun, and P. Kisilev, “Deep learning for decoding of linear codes - a syndrome-based approach,” arXiv:1802. 04741v1, Feb. 2018. T. Gruber, S. Cammerer, J. Hoydis, and S. ten Brink, “On deep learning-based channel decoding,” in Proc. 2017 51st Annual Conference on Information Sciences and Systems (CISS), pp. 1-6, Baltimore, MD, USA, Mar. 2017. W. Lyu, Z. Zhang, C. Jiao, K. Qin, and H. Zhang, “Performance evaluation of channel decoding with deep neural networks,” arXiv:1711.00727v1, Nov. 2017. K. Niu, K. Chen, and J. Lin, “Beyond turbo codes: rate-compatible punctured polar codes,” in Proc. 2013 IEEE Int. Conf. Commun., pp. 3423-3427, Budapest, Hungary, Jun. 2013.

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UCI(KEPA) : I410-ECN-0101-2018-567-003342079