报告题目: Ten Challenges in Advancing Machine Learning Technologies towards 6G
报告人：Kato Nei，日本东北大学信息科学学院院长，日本工程院院士，IEEE/IEICE Fellow
报告时间：2022年12月28日 16：00-17:30
报告地点：zoom在线会议 会议ID：88305703804 密码： 305752
https://us02web.zoom.us/j/88305703804?pwd=czRIcG1veTRuWkpHc1d1ckwxcjNRdz09
报告人简介：Nei Kato is a full professor and the Dean with Graduate School of Information Sciences, Tohoku University. He has researched on computer networking, wireless mobile communications, satellite communications, ad hoc & sensor & mesh networks, UAV networks, smart grid, AI, IoT, Big Data, and pattern recognition. He has published more than 500 papers in prestigious peer-reviewed journals and conferences. He is the Director of Magazine of IEEE Communications Society. He served as the Vice-President (Member & Global Activities) of IEEE Communications Society (2018-2021), the Editor-in-Chief of IEEE Network Magazine (2015-2017), and the Editor-in-Chief of IEEE Transactions on Vehicular Technology (2017-2021). He is a Distinguished Lecturer of IEEE Communications Society and Vehicular Technology Society, a Fellow of the Engineering Academy of Japan, a Fellow of IEEE, and a Fellow of IEICE.
报告内容:
As the 5G standard is being completed, the academia and industry have begun to consider more developed cellular communication technique, 6G, which is expected to achieve high data rate up to 1Tb/s and broad frequency bands of 100GHz to 3THz. Besides the significant upgrade of the key communication metrics, Artificial Intelligence (AI) has been envisioned by many researchers as the most important feature of 6G, since the state-of-the-art machine learning technique has been adopted as the top solution in many extremely complex scenarios. Network intelligentization will be the new trend to address the challenges of exponentially increasing number of connected heterogeneous devices. However, compared with the application of machine learning in other fields, such as computer games, current research on intelligent networking still has a long way to go for realizing the automatically-configured cellular communication systems. Various problems in terms of communication system, machine learning architectures, and computation efficiency should be addressed for the full use of this technique in 6G. In this talk, I will introduce ten most critical challenges in advancing the intelligent 6G system. These challenges are analyzed from the perspectives of 6G service requirements, AI algorithm design, practical deployment, and future standardization.
