Machine Learning in 6G Wireless Communications

被引:13
|
作者
Ohtsuki, Tomoaki [1 ]
机构
[1] Keio Univ, Fac Sci & Technol, Yokohama 2238522, Japan
关键词
artificial intelligence (AI); machine learning (ML); deep learning (DL); neural network (NN); deep neural network (DNN); 6G; deep transfer learning (DTL); CHANNEL ESTIMATION; DEEP; ALGORITHM; ACCESS;
D O I
10.1587/transcom.2022CEI0002
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Mobile communication systems are not only the core of the Information and Communication Technology (ICT) infrastructure but also that of our social infrastructure. The 5th generation mobile communication system (5G) has already started and is in use. 5G is expected for various use cases in industry and society. Thus, many companies and research institutes are now trying to improve the performance of 5G, that is, 5G Enhancement and the next generation of mobile communication systems (Beyond 5G (6G)). 6G is expected to meet various highly demanding requirements even compared with 5G, such as extremely high data rate, extremely large cover-age, extremely low latency, extremely low energy, extremely high reliability, extreme massive connectivity, and so on. Artificial intelligence (AI) and machine learning (ML), AI/ML, will have more important roles than ever in 6G wireless communications with the above extreme high requirements for a diversity of applications, including new combinations of the requirements for new use cases. We can say that AI/ML will be essential for 6G wireless communications. This paper introduces some ML techniques and appli-cations in 6G wireless communications, mainly focusing on the physical layer.
引用
收藏
页码:75 / 83
页数:9
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