Workshop 27: Recent Advances for Machine Learning in Wireless Communication

Keywords: Machine Learning, Wireless Communication, Deep Learning, Resource Allocation

Machine learning methods have recently received much attention as a key enabler for wireless communication and networks. Yet, how to apply machine learning algorithms in real-world networks still faces a lot of challenges, and much of the realization of the promised benefits requires thorough research and development. This workshop will focus on machine learning solutions to problems in wireless communication and networks, across various layers and within a broad range of applications. Its topics of interest include, but are not limited to:
Machine/deep learning for signal detection, channel modeling, estimation, interference mitigation, and decoding
Resource allocation and network optimization using machine learning techniques
Distributed machine learning for wireless communications
Deep reinforcement learning for wireless communications
Graph Neural Networks for wireless communications
Machine learning based testbeds and experimental evaluations
Chair: Assoc. Prof. Xin Yan |
Wuhan University of Technology
Jun Xiong received his B.S. and Ph.D. degrees from National University of Defense Technology (NUDT), China, in 2009 and 2014, respectively. He is currently an associate professor in School of Electronic Science and Technology, NUDT. His main research interests are artificial intelligent and machine learning for wireless communication, physical layer security, and cooperative communication. Moreover, he has published more than 60 papers on top international conferences and journals in recent years including IEEE IoT, TVT, TIFS, GLOBECOM, etc. He serves as a reviewer for IEEE Transactions on Communications, IEEE Transactions on Vehicular Technology, IEEE Communications Letters, and IEEE Wireless Communications Letters. He was the Technical Program Committee member for many international conferences, such as GLOBECOM, ICC, WCNC, WCSP, and ICCC.