Dr. Yuanjian Li (李元健) has been serving as an Assistant Professor in the Department of Communications and Networking, School of Advanced Technology, Xi'an Jiaotong-Liverpool University (XJTLU), Suzhou, China, since March 2025. He received his PhD degree from King's College London (KCL), UK. Prior to joining XJTLU, Dr. Li held research positions at several institutions worldwide, including Research Fellow at Nanyang Technological University (Singapore), Research Associate at Heriot-Watt University (UK), and Research Assistant at the University of Warwick (UK).

As a core research team member, Dr. Li actively contributed to securing and executing four large-scale research projects with a total funding exceeding CNY 50 million. These projects, supported by notable funding bodies such as the National Research Foundation (NRF) of Singapore, Infocomm Media Development Authority (IMDA) of Singapore, and the Engineering and Physical Sciences Research Council (EPSRC) of the UK, focus on next-generation wireless communication technologies, specifically deep reinforcement learning-enabled wireless resource management, drone-assisted Internet of Things (IoT), and space-air-ground integrated networks.

Dr. Li has published over 20 papers in prestigious journals and leading international conferences at the intersection of wireless communications, signal processing, machine learning, and quantum computing. As the first author, he has contributed 12 papers to top-tier venues, including IEEE Transactions on Wireless Communications (3 papers), IEEE Transactions on Communications (2 papers), IEEE Wireless Communications Letters (1 paper), IEEE Global Communications Conference (3 papers), IEEE International Conference on Communications (2 papers), and IEEE Personal, Indoor and Mobile Radio Communications (1 paper). Among these, he is the corresponding author for 5 journal publications. Additionally, Dr. Li is actively involved in peer-reviewing activities for leading journals and conferences within his research scope.

Dr. Li has authored 9 patents in the fields of wireless communications and signal processing. He has also taken active roles in professional activities, serving as a session chair for IEEE ICC 2022 (Seoul, South Korea) in the Selected Areas in Communications: Machine Learning for Communications Track - Networks, and IEEE GLOBECOM 2024 (Cape Town, South Africa) in Machine Learning for UAVs. Furthermore, he has contributed as a Technical Program Committee (TPC) member for the International Conference on Internet of Things (ICIoT) 2024.

My interdisciplinary research focuses on AI for Next-Generation Wireless Systems.

My long-term goal is to build an Ecosystem of Wireless, AI, and Quantum Computing.

Two zoomed-in examples are

• Deep Reinforcement Learning (DRL)-enabled joint computation and communication resource coordination for many-UAV many-user multi-access edge computing (MEC) in the Internet of Things (IoT) scenarios.
• Model-driven machine learning (ML)-aided sparsity-aware channel estimation and efficient receiver design for Terahertz (THz) ultra-massive multiple-input multiple-output (UM-MIMO) transmissions, where near-field communication characteristics are highlighted.

Broadly speaking, my research expertise and interests include

1. Artificial Intelligence (AI)-Native Sixth-Generation (6G) Wireless Systems
2. Internet of Intelligent Things (IoT)
3. Non-Terrestrial Communications (e.g., Drone-Aided Networks)
4. (Scalable/Multi-Agent) DRL-Driven Joint Communication and Computation Resource Management for Multi-Access Edge Computing
5. Compressive Sensing (CS)- and Machine Learning (ML)-Aided Channel Estimation for THz UM-MIMO Systems
6. Quantum Machine Learning (QML) for Next-Generation Wireless Systems
7. Secure and Covert Communications

Senior Reviewer for IEEE Open Journal of the Communications Society, since 2025

Session Chair for IEEE GLOBECOM’2024-SAC-AC-S01 : Machine learning for UAVs

Technical Program Committee (TPC) Member for International Conference on Internet of Things 2024 (ICIoT 2024)

Session Chair for IEEE ICC’2022-SAC-05 Machine Learning for Communications Track-Networks

Prospective PhD applicants are welcomed to contact me for informal discussion!