The recent upsurge of diversified mobile applications, especially those supported by AI, is spurring heated discussions on the future evolution of wireless communications. While 5G is being deployed around the world, efforts from industry and academia have started to look beyond 5G and conceptualize 6G. We envision 6G to undergo an unprecedented transformation that will make it substantially different from the previous generations of wireless cellular systems. In particular, 6G will go beyond mobile Internet and will be required to support ubiquitous AI services from the core to the end devices of the network. Meanwhile, AI will play a critical role in designing and optimizing 6G architectures, protocols, and operations. In this talk, we discuss potential technologies for 6G to enable mobile edge AI applications, including over-the-air computation for federated learning. AI-enabled methodologies for 6G network design and optimization are also introduced, including the “learning to optimize” framework for solving the large-scale optimization problems via deep learning.
Yuanming Shi (S’13-M’15-SM’20) received the B.S. degree in electronic engineering from Tsinghua University, Beijing, China, in 2011. He received the Ph.D. degree in electronic and computer engineering from The Hong Kong University of Science and Technology (HKUST), in 2015. Since September 2015, he has been with the School of Information Science and Technology in ShanghaiTech University, where he is currently a tenured Associate Professor. He visited University of California, Berkeley, CA, USA, from October 2016 to February 2017. Dr. Shi is a recipient of the 2016 IEEE Marconi Prize Paper Award in Wireless Communications, and the 2016 Young Author Best Paper Award by the IEEE Signal Processing Society. He is an editor of IEEE Transactions on Wireless Communications and IEEE Journal on Selected Areas in Communications. His research areas include optimization, statistics, machine learning, signal processing, and their applications to 6G, IoT, and AI.