Potential technologies and applications based on deep learning in the 6G networks. (October 2021)

• Record Type: Journal Article
• Authors: Zheng, Zunxin; Wang, Linmei; Zhu, Fumin; Liu, Ling
• Is Part Of: Computers & electrical engineering. Volume 95(2021)
• Publication Date: 2021-10
• Subjects: Deep learning -- 6G networks -- IoT -- Big data -- Cloud computing; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107373
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: 5G networks might not meet the growing demands of Internet of Things. It is necessary to develop 6G networks for intelligent society in the future. 6G networks have powerful functions but face a lot of technical challenges. Deep learning could promote the achievement of 6G networks with the help of big data and cloud computing. Abstract: The fifth generation (5G) networks provide high speed, low latency, and reliable communication services, and have been making great differences in our daily life. However, it might be difficult to meet the growing demands of Internet of Things (IoT) devices based on the existing technologies in the future, so the concept of sixth generation (6G) networks were raised to improve the existing 5G networks and promote the development of intelligent applications furthermore. Therefore, we introduce the characteristics, challenges, and potential technologies of 6G networks, and find that deep learning is potential to overcome these challenges with the help of big data and cloud computing. In addition, Deep learning approaches and potential applications in 6G networks are also discussed. We summarize the important viewpoints and studies, and provide some new ideas for developing 6G networks. Graphical Abstract: Image, graphical abstract