Recent Advances in Deep Learning for Channel Coding: A Survey
This paper provides a comprehensive survey of recent advances in deep learning (DL) techniques for channel coding problems. Inspired by the recent successes of DL in a variety of research domains, its applications to physical layer technologies have been extensively studied in recent years, and they...
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| Vydáno v: | IEEE open journal of the Communications Society Ročník 5; s. 6443 - 6481 |
|---|---|
| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
New York
IEEE
2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Témata: | |
| ISSN: | 2644-125X, 2644-125X |
| On-line přístup: | Získat plný text |
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| Abstract | This paper provides a comprehensive survey of recent advances in deep learning (DL) techniques for channel coding problems. Inspired by the recent successes of DL in a variety of research domains, its applications to physical layer technologies have been extensively studied in recent years, and they are expected to be a potential breakthrough in supporting the emerging use cases of the next generation wireless communication systems such as 6G. In this paper, we focus exclusively on channel coding problems and review existing approaches that incorporate advanced DL techniques into code design and channel decoding. After briefly introducing the background of recent DL techniques, we categorize and summarize a variety of approaches, including model-free and model-based DL, for the design and decoding of modern error-correcting codes, such as low-density parity check (LDPC) codes and polar codes, to highlight their potential advantages and challenges. Finally, the paper concludes with a discussion of open issues and future research directions in channel coding. |
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| AbstractList | This paper provides a comprehensive survey of recent advances in deep learning (DL) techniques for channel coding problems. Inspired by the recent successes of DL in a variety of research domains, its applications to physical layer technologies have been extensively studied in recent years, and they are expected to be a potential breakthrough in supporting the emerging use cases of the next generation wireless communication systems such as 6G. In this paper, we focus exclusively on channel coding problems and review existing approaches that incorporate advanced DL techniques into code design and channel decoding. After briefly introducing the background of recent DL techniques, we categorize and summarize a variety of approaches, including model-free and model-based DL, for the design and decoding of modern error-correcting codes, such as low-density parity check (LDPC) codes and polar codes, to highlight their potential advantages and challenges. Finally, the paper concludes with a discussion of open issues and future research directions in channel coding. |
| Author | Ochiai, Hideki Matsumine, Toshiki |
| Author_xml | – sequence: 1 givenname: Toshiki orcidid: 0000-0001-9583-8129 surname: Matsumine fullname: Matsumine, Toshiki email: toshiki.matsumine.jp@ieee.org organization: Institute of Advanced Sciences, Yokohama National University, Yokohama, Japan – sequence: 2 givenname: Hideki orcidid: 0000-0001-9303-5250 surname: Ochiai fullname: Ochiai, Hideki organization: Graduate School of Engineering, Osaka University, Osaka, Japan |
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| CODEN | IOJCAZ |
| CitedBy_id | crossref_primary_10_1016_j_comcom_2025_108287 crossref_primary_10_3390_fi17040181 crossref_primary_10_1109_JETCAS_2025_3561330 |
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| SubjectTerms | Channel coding Codes Coding Communication systems Decoding Deep learning deep learning (DL) Error correcting codes Error correction Graphics processing units low-density parity check (LDPC) codes Machine learning machine learning (ML) neural network Neural networks Physical layer polar codes Reviews Surveys turbo codes Wireless communication systems |
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| Title | Recent Advances in Deep Learning for Channel Coding: A Survey |
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