Prof. Yue Gao
Title: Beamforming and Channel Tracking for Space-Air-Ground Integrated Network
Bio:
Yue Gao is a Professor and Chair in Wireless Communications at Institute for Communication Systems, University of Surrey, United Kingdom. He received the Ph.D. degree from the Queen Mary University of London U.K., in 2007. He currently leads the antennas and signal processing lab and develops fundamental research into practice in the interdisciplinary area of smart antennas, signal processing, spectrum sharing, millimetre-wave and Internet of Things technologies in mobile and satellite systems. He has published over 200 peer-reviewed journal and conference papers, 3 patents, 1 book and 5 book chapters and 3 best paper awards. He is an Engineering and Physical Sciences Research Council Fellow from 2018 to 2023. He was a co-recipient of the EU Horizon Prize Award on Collaborative Spectrum Sharing in 2016 and shortlisted for the Newton Prize on IoT systems for smart farming in 2019. He served as the Signal Processing for Communications Symposium Co-Chair for IEEE ICCC 2016, the Publicity Co-Chair for the IEEE GLOBECOM 2016, the Cognitive Radio Symposium Co-Chair for the IEEE GLOBECOM 2017, and the General Chair of the IEEE WoWMoM and iWEM 2017. He is the Chair of the IEEE Technical Committee on Cognitive Networks, the Secretary of the IEEE ComSoc Technical Committee Wireless Communication and the IEEE Distinguished Lecturer of the Vehicular Technology Society. He is an Editor for the IEEE INTERNET OF THINGS JOURNAL, IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY and IEEE TRANSACTIONS ON COGNITIVE NETWORKS.
Abstract:
The space-air-ground integrated network (SAGIN) aims to provide seamless wide area connections, high throughput and strong resilience for 5G and beyond communications. Acting as a crucial link segment of the SAGIN, unmanned aerial vehicle (UAV)-satellite communication has drawn much attention. However, it is a key challenge to track dynamic channel information due to the low earth orbit (LEO) satellite orbiting and three- dimensional (3D) UAV trajectory. This presentation will explore the 3D channel tracking for a Ka-band UAV-satellite communication system. A statistical dynamic channel model, called 3D two-dimensional Markov model (3D-2D-MM), will discussed for the UAV- satellite communication system by exploiting the probabilistic insight relationship of both hidden value vector and joint hidden support vector. Specifically, for the joint hidden support vector, the more realistic 3D support vector in both azimuth and elevation direction will be considered. A 3D dynamic turbo approximate message passing (3D-DTAMP) algorithm will be introduced to recursively track the dynamic channel with the 3D-2D-MM priors. Finally, channel tracking performance will be compared with the state-of-the-art algorithms in terms of lower pilot overhead and complexity.