My interdisciplinary research focuses on AI for Next-Generation Wireless Systems.
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.
The best way to know me is to check the latest CV file. Besides, copies of my PhD and MEng theses are accessible in PhD Thesis and MEng Thesis, respectively.
Accepting PhD students! Please feel free to drop me an email to reach out at yuanjian.li@xjtlu.edu.cn; You can find more information about me by visiting my faculty page
- (as Primary PhD Supervisor) Postgraduate Research Scholarship (PGRS)-Funded PhD Project at XJTLU, FOSA2506034, DRL-Enabled Resource Coordination for Covertness-Aware and Energy-Efficient UAV-Aided IoT, CNY 297, 000
- (as Primary PhD Supervisor) XJTLU-XJTU-UoL Joint Doctoral Supervision Project, SFXJTU2506, Quantum Deep Reinforcement Learning-Aided Resource Coordination for Energy-Efficient 6G Networks (XJTU and UoL are abbreviations for Xi’an Jiaotong University and the University of Liverpool, respectively)
- (as Second PhD Supervisor) PGRS-Funded PhD Project at XJTLU, FOSLG250407, Adaptive Digital Twin Modelling and Optimization for V2X Networks in Large-Scale Traffic Scenarios, since 2025-07, CNY 297, 000