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 |
| Published: |
Wiley
2023-10-01
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| Series: | IET Communications |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cmu2.12669 |
| Summary: | 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. |
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| ISSN: | 1751-8628 1751-8636 |