Deep learning in physical layer communications: Evolution and prospects in 5G and 6G networks
Abstract With the rapid development of the communication industry in the fifth generation and the advance towards the intelligent society of the sixth generation wireless networks, traditional methods are unable to meet the ever‐growing demands for higher data rates and improved quality of service....
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-10-01
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| Series: | IET Communications |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cmu2.12669 |
| _version_ | 1827800876797919232 |
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| author | Chengchen Mao Zongwen Mu Qilian Liang Ioannis Schizas Chenyun Pan |
| author_facet | Chengchen Mao Zongwen Mu Qilian Liang Ioannis Schizas Chenyun Pan |
| author_sort | Chengchen Mao |
| collection | DOAJ |
| description | Abstract With the rapid development of the communication industry in the fifth generation and the advance towards the intelligent society of the sixth generation wireless networks, traditional methods are unable to meet the ever‐growing demands for higher data rates and improved quality of service. Deep learning (DL) has achieved unprecedented success in various fields such as computer vision, large language model processing, and speech recognition due to its powerful representation capabilities and computational convenience. It has also made significant progress in the communication field in meeting stringent demands and overcoming deficiencies in existing technologies. The main purpose of this article is to uncover the latest advancements in the field of DL‐based algorithm methods in the physical layer of wireless communication, introduce their potential applications in the next generation of communication mechanisms, and finally summarize the open research questions. |
| first_indexed | 2024-03-11T20:17:00Z |
| format | Article |
| id | doaj.art-189c95a2656b435abdbf68a281349f12 |
| institution | Directory Open Access Journal |
| issn | 1751-8628 1751-8636 |
| language | English |
| last_indexed | 2024-03-11T20:17:00Z |
| publishDate | 2023-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Communications |
| spelling | doaj.art-189c95a2656b435abdbf68a281349f122023-10-03T10:55:37ZengWileyIET Communications1751-86281751-86362023-10-0117161863187610.1049/cmu2.12669Deep learning in physical layer communications: Evolution and prospects in 5G and 6G networksChengchen Mao0Zongwen Mu1Qilian Liang2Ioannis Schizas3Chenyun Pan4Department of Electrical Engineering The University of Texas at Arlington Texas USAAutoX, Inc. San Jose California USADepartment of Electrical Engineering The University of Texas at Arlington Texas USADepartment of Electrical Engineering The University of Texas at Arlington Texas USADepartment of Electrical Engineering The University of Texas at Arlington Texas USAAbstract With the rapid development of the communication industry in the fifth generation and the advance towards the intelligent society of the sixth generation wireless networks, traditional methods are unable to meet the ever‐growing demands for higher data rates and improved quality of service. Deep learning (DL) has achieved unprecedented success in various fields such as computer vision, large language model processing, and speech recognition due to its powerful representation capabilities and computational convenience. It has also made significant progress in the communication field in meeting stringent demands and overcoming deficiencies in existing technologies. The main purpose of this article is to uncover the latest advancements in the field of DL‐based algorithm methods in the physical layer of wireless communication, introduce their potential applications in the next generation of communication mechanisms, and finally summarize the open research questions.https://doi.org/10.1049/cmu2.12669deep learningfifth generationphysical layersixth generationwireless communications |
| spellingShingle | Chengchen Mao Zongwen Mu Qilian Liang Ioannis Schizas Chenyun Pan Deep learning in physical layer communications: Evolution and prospects in 5G and 6G networks IET Communications deep learning fifth generation physical layer sixth generation wireless communications |
| title | Deep learning in physical layer communications: Evolution and prospects in 5G and 6G networks |
| title_full | Deep learning in physical layer communications: Evolution and prospects in 5G and 6G networks |
| title_fullStr | Deep learning in physical layer communications: Evolution and prospects in 5G and 6G networks |
| title_full_unstemmed | Deep learning in physical layer communications: Evolution and prospects in 5G and 6G networks |
| title_short | Deep learning in physical layer communications: Evolution and prospects in 5G and 6G networks |
| title_sort | deep learning in physical layer communications evolution and prospects in 5g and 6g networks |
| topic | deep learning fifth generation physical layer sixth generation wireless communications |
| url | https://doi.org/10.1049/cmu2.12669 |
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