Papers
13
Total Citations
377
H-Index
8
About
Zhu Han is a prolific researcher whose work spans wireless communications, autonomous systems, and intelligent networking — areas where cutting-edge mathematics meets real-world deployment challenges. He is perhaps best known for pioneering the application of game-theoretic frameworks to distributed wireless systems, exemplified by his highly cited 2010 work on hedonic coalition formation for autonomous wireless agents, which has garnered 144 citations and laid foundational groundwork for coordinating unmanned aerial vehicles and cognitive radio devices in next-generation networks. Han's contributions extend into mobile crowdsensing, where he has employed mean-field game theory to optimize task assignment and collision-free trajectory planning for UAV and robot networks. More recently, his research has embraced emerging paradigms including O-RAN slicing for Industrial IoT, reconfigurable intelligent surfaces combined with federated learning, and deep reinforcement learning for resource allocation — reflecting his ability to stay at the frontier of rapidly evolving fields. His 2022 work on elastic O-RAN slicing has already attracted 56 citations, underscoring its relevance to industrial connectivity. Across his portfolio, Han demonstrates a rare talent for synthesizing rigorous theoretical tools — from actor-critic reinforcement learning to compressed sensing — with pressing practical problems, making his research invaluable to students and practitioners advancing intelligent, autonomous wireless systems.
Research Focus
Key Achievements
Top Papers
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- 9Effective Multi-Agent Communication Under Limited Bandwidth8 citations · 2023
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