报告题目：Intrinsic Intelligence in Mobile Communication Networks

报告时间：2024年7月26日 10：00

报告地点：87978797威尼斯老品牌中心校区王湘浩楼A521报告厅

报告人：杨鲲教授 英国埃塞克斯大学

报告人简介：

Kun Yang is currently a Chair Professor with the School of Computer Science and Electronic Engineering, University of Essex, U.K., leading the Network Convergence Laboratory (NCL). He is also an affiliated Professor with Nanjing University and UESTC, China. He received his PhD from the Department of Electronic & Electrical Engineering of University College London (UCL), UK. He has managed research projects funded by U.K. EPSRC, EU FP7/H2020, and industries. His main research interests include wireless networks and communications, future internet, edge computing, and energy aspects of future communication systems, such as 6G, promoting energy self-sustainability via both energy efficiency (green communications and networks), and energy harvesting (wireless charging). He has published 500+ papers and filed 50+ patents. He serves on the editorial boards of a number of IEEE journals (e.g., IEEE TNSE, TVT, ComMag, WCL). He is a Deputy Editor-in-Chief of IET Smart Cities Journal. He has been a Judge of GSMA GLOMO Award at World Mobile Congress – Barcelona since 2019. He was a Distinguished Lecturer of IEEE ComSoc (2020-2021). He is a Member of Academia Europaea (MAE), a Fellow of IEEE, a Fellow of IET and a Distinguished Member of ACM.

报告内容简介：

The lecture focuses on intrinsic intelligence in future mobile communication networks, beginning with a brief overview of the evolution towards 6G systems. It highlights current challenges faced by communication networks, followed by an introduction to leveraging next-generation artificial intelligence (AI) technologies such as deep neural networks and machine learning to enhance intrinsic intelligence in communication networks. The discussion includes specific implementation suggestions and examples of enabling technologies, such as data-driven end-to-end communication systems, semantics-oriented weak communication, network digital twins, and large-scale model communication systems.

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