ML-enhanced distributed sensing and communication in multi-RIS augmented wireless propagation environments
Hosted by and enrolled in the Doctoral Program at Technische Universität Wien.
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Description of research
The objective of this research project is to enhance communication and sensing capabilities in multi-RIS augmented wireless propagation environments by applying advanced machine learning (ML)-based signal processing techniques. Single RIS deployments, constrained by coarse phase quantisation and limited observation coverage, provide limited support for sensing applications. However, fusing even coarse observations from multiple distributed RISs has the potential to provide remarkably accurate and robust sensing capabilities, even in non-line-of-sight propagation scenarios. The primary objective of this research is to harness the power of multi-RIS augmented ISAC systems by integrating ML-enhanced signal processing methods to combine sensing data from base stations with complementary information from the RISs, such as their optimised phase configurations. This research focuses on ultra-wideband transmissions, considering the frequency-selective nature of real-world RIS responses, which provide high temporal resolution. It will also explore the latest technological advances in RIS, including non-diagonal RIS and STAR-RIS. The project also targets high-mobility scenarios in vehicular and UAV-based networks, addressing the unique challenges posed by these dynamic environments.
Short bio of DC
Chonghao Zhao is a doctoral candidate at the Institute of Telecommunications, within the Faculty of Electrical Engineering and Information Technology, Technische Universität Wien. As part of the ISAC-Newton MSCA-DN project, his research focuses on developing cooperation techniques for distributed wireless networks by fusing signal and data from base stations and complementary information from the RISs. He aims to harness the power of multi-RIS augmented ISAC systems by integrating ML-enhanced signal processing methods to enhance robustness and reliability in challenging wireless propagation environments.
Chonghao holds a Master’s degree in Information and Communication Engineering from the University of Electronic Science and Technology of China, and a Bachelor’s degree in Electronic and Information Engineering from Sichuan University. His expertise includes array processing, statistical learning, applied optimization, channel modeling, and simulation.
He is passionate about combining classical methods with emerging learning-based approaches to bridge the gap between academia and industry in next-generation wireless technologies.
Simplified Summary
This page is about PhD student Chonghao Zhao. He studies at TU Wien in Austria.