Papers

9

Total Citations

142

H-Index

5

About

Xiao Liu is a researcher at the forefront of next-generation wireless communications and intelligent robotics, with a particular focus on non-orthogonal multiple access (NOMA) techniques, reconfigurable intelligent surfaces (RIS), and 5G/Beyond-5G network design. His most impactful work explores the integration of mobile RIS-equipped robots into indoor wireless networks, leveraging federated learning to enhance service quality for mobile users — a contribution that has garnered 48 citations and represents a significant step toward adaptive, AI-driven communication infrastructure. Liu has also made notable strides in robotic communications, authoring a widely recognized survey on 5G/B5G-enabled terrestrial robotics that has accumulated 34 citations across multiple iterations. His research on NOMA-enhanced indoor intelligent robots addresses critical challenges in path design and resource management, while his work on integrated communication, control, and computing (3C) frameworks advances the reliability of remote e-health systems. More recently, Liu has expanded into embodied AI and human-robot interaction, introducing diffusion-based imitation learning policies and smartwatch-driven robot control systems. Collectively, his portfolio reflects a unique cross-disciplinary vision connecting advanced wireless networking with practical robotics applications.

Research Focus

Key Achievements

5
H-Index
9
Papers
142
Total Citations
16
Avg Citations/Paper
🏆 Most Cited Paper
Mobile Reconfigurable Intelligent Surfaces for NOMA Networks: Federated Learning Approaches
48 citations · 2022
📈 Most Prolific Year: 2021 (3 Papers)
🤝 Key Collaborators: 22
🏛 Institutions: Queen Mary University of London, Arizona State University

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago