{"title":"信道编码深度学习的最新进展:调查","authors":"Toshiki Matsumine;Hideki Ochiai","doi":"10.1109/OJCOMS.2024.3472094","DOIUrl":null,"url":null,"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.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10702508","citationCount":"0","resultStr":"{\"title\":\"Recent Advances in Deep Learning for Channel Coding: A Survey\",\"authors\":\"Toshiki Matsumine;Hideki Ochiai\",\"doi\":\"10.1109/OJCOMS.2024.3472094\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":33803,\"journal\":{\"name\":\"IEEE Open Journal of the Communications Society\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.3000,\"publicationDate\":\"2024-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10702508\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Open Journal of the Communications Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10702508/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Open Journal of the Communications Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10702508/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
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 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.
期刊介绍:
The IEEE Open Journal of the Communications Society (OJ-COMS) is an open access, all-electronic journal that publishes original high-quality manuscripts on advances in the state of the art of telecommunications systems and networks. The papers in IEEE OJ-COMS are included in Scopus. Submissions reporting new theoretical findings (including novel methods, concepts, and studies) and practical contributions (including experiments and development of prototypes) are welcome. Additionally, survey and tutorial articles are considered. The IEEE OJCOMS received its debut impact factor of 7.9 according to the Journal Citation Reports (JCR) 2023.
The IEEE Open Journal of the Communications Society covers science, technology, applications and standards for information organization, collection and transfer using electronic, optical and wireless channels and networks. Some specific areas covered include:
Systems and network architecture, control and management
Protocols, software, and middleware
Quality of service, reliability, and security
Modulation, detection, coding, and signaling
Switching and routing
Mobile and portable communications
Terminals and other end-user devices
Networks for content distribution and distributed computing
Communications-based distributed resources control.