Abstract
Smart future requires a fully connected world and provide a high data rate and dependable wireless connectivity for all intelligent devices and services, in terms of communications, sensing, and localization. In response to these requirements, highly accurate channel data need to be realized in various scenarios at all frequency bands. This paper presents the recent progress on hyper ray tracing for high-fidelity channel modeling, which is also a key enabler for smart wireless environments. To begin with, electromagnetic (EM) property and propagation mechanism libraries need to be built for various materials at different bands, from propagation mechanism measurements. Then, through extensive simulations using a high-performance ray tracing platform, realistic channel data can be obtained for communication channels in challenging conditions and multi-band sensing signals. Moreover, machine learning models for super-resolution wireless channel characteristics are presented to show how artificial intelligence can help transferring from high-performance ray tracer to hyper ray tracer in terms of increasing the efficiency of generating channel data while keeping high fidelity. Last but not least, some demos are shown that in the future, hyper ray tracer is promising to offer complete, real-time, accurate, and reliable multipath propagation data that projects the EM environment of the physical world in all aspects to the cyber space.
Short bio:
Ke Guan received B.E. degree and Ph.D. degree from Beijing Jiaotong University in 2006 and 2014, respectively. He is a Professor at the State Key Laboratory of Advanced Rail Autonomous Operation and the School of Electronic and Information Engineering, Beijing Jiaotong University, and a Research Advisor at Jožef Stefan Institute, Slovenia. In 2016, he was awarded a Humboldt Research Fellowship for Postdoctoral Researchers. From February 2023 to July 2023, he was a Guest Professor at Technische Universitaet Wien, Austria. In 2024, he has been elected as Fellow of the Royal Asiatic Society of Great Britain and Ireland (FRAS). He has authored/coauthored two books and five book chapters, more than 200 journal and conference papers, and ten patents. His current research interests include the measurement and modeling of wireless propagation channels, high-speed railway communications, and digital twin of electromagnetic environments in various complex scenarios based on ray-tracing and machine learning, such as vehicle-to-x communications, terahertz communication systems, integrated sensing and communications, and space-air-ground integrated networks.
Prof. Guan is the pole leader of EURNEX (European Railway Research Network of Excellence). He was the recipient of the 2014 International Union of Radio Science (URSI) Young Scientist Award, the 2024 IEEE ITSS DEIB Fellowship, the 2023 Emerald Global Outstanding Award, and the 2023 JIMSE Global Young Scientist Award in Advanced Manufacturing. He is listed in the World’s Top 2% Scientists (both for the single year 2022 and the whole career). His papers received 14 Best Paper Awards, including the IEEE Vehicular Technology Society Neal Shepherd Memorial Best Propagation Paper Award in 2019 and 2022. He is an Editor of IEEE Vehicular Technology Magazine and IET Microwave, Antenna and Propagation, and a Guest Editor of the IEEE Transactions on Vehicular Technology and IEEE Communication Magazine. He serves as a Publicity Chair in PIMRC 2016, the Publicity Co-Chair in ITST 2018, the Track Co-Chair in EuCNC 2018 and 2024, the International Liaison of EUSIPCO 2019, the Session Convener of EuCAP 2015-2024, and a TPC Member for many IEEE conferences, such as Globecom, ICC, VTC, etc. He is the contact person of Beijing Jiaotong University in 3GPP and ETSI as well as a member of the COST IC1004, CA15104, and CA20120 initiatives.