Research Area
Distributed Machine Learning Over Wireless Networks
Develop efficient communication techniques for distributed machine learning (e.g., federated learning, distributed edge learning) over wireless networks
Develop and analyze reception algorithms for federated learning
Optimize radio resource allocation and user scheduling for federated learning with multiple devices
Physical-Layer Techniques for Future Wireless Systems
Develop data detection and channel estimation techniques for massive MIMO and millimeter Wave communications
Characterize the fundamental limits of future interfering networks under practical constraints
Design a future communication framework to support massive connectivity, extremely capacity, and high reliability
Machine-Learning-Based Communication Techniques
Explore a data-driven communication framework for IoT networks, low-latency communications, and low-power sensor networks
Develop robust transmission/reception algorithms based on reinforcement learning
Optimize cell association, user clustering, and radio resource allocation via stochastic optimizaiton