Professor, Zhejiang University of Technology, China
Dr. Li Ping Qian is a distinguished Professor at the College of Information Engineering, Zhejiang University of Technology, Hangzhou, China. As an IEEE Senior Member and IEEE VTS Distinguished Lecturer (2024-2026), he is a leading researcher in wireless networks, edge intelligence, and emerging multiple access techniques. With a Ph.D. from The Chinese University of Hong Kong, his contributions to deep learning-powered communication networks have been widely recognized. His expertise in mobile edge computing and resource management has established him as a thought leader in the field.
Publication Profile
🎓 Education
Dr. Li Ping Qian earned his Ph.D. in Information Engineering from The Chinese University of Hong Kong in 2010. Prior to that, he obtained his Master of Engineering in Electronic Science and Technology (2006) and Bachelor of Engineering in Information Engineering (2004) from Zhejiang University, Hangzhou, China. His strong academic foundation has propelled his research in advanced communication networks.
💼 Experience
Dr. Qian is currently a Jianxing Distinguished Professor at Zhejiang University of Technology, a role he has held since 2022. Previously, he served as a Full Professor (2018-2021) and Associate Professor (2012-2017) at the same institution. He also leads as the Director of the Information and Communication Discipline. His international experience includes being a Visiting Scholar at the University of Waterloo (2016-2017) and a Research Collaborator at Princeton University (2009). His academic journey began as a Postdoctoral Research Associate at The Chinese University of Hong Kong (2010-2011).
🏆 Awards and Honors
Dr. Qian has received multiple prestigious Best Paper Awards, including the Best Conference Paper Award at UCom 2024 and IEEE ICCT 2023 for his work on integrated sensing and communication in edge computing networks. He has also secured significant research grants, including funding from the Natural Science Foundation of Zhejiang Province for multi-user scheduling and relay selection in 5G networks.
🔬 Research Focus
Dr. Qian’s research interests span deep learning-driven communication networks, edge intelligence, and mobile edge computing for IoT. His work delves into optimizing resource management in wireless networks and developing novel multiple access techniques for efficient data transmission. His innovative contributions bridge artificial intelligence and wireless communications, making him a key figure in the evolution of next-generation networks.
🔍 Conclusion
Dr. Li Ping Qian is a highly accomplished researcher whose contributions to deep learning, mobile edge computing, and next-generation wireless networks have shaped the field of modern communication systems. His extensive experience, prestigious awards, and impactful publications solidify his position as a leader in information engineering. 🚀
📝 Publications
Diffusion-Based Radio Signal Augmentation for Automatic Modulation Classification, Electronics, 2024 (DOI)
A Survey on Integrated Sensing, Communication, and Computing Networks for Smart Oceans, Journal of Sensor and Actuator Networks, 2022 (DOI)
Adaptive Facial Imagery Clustering via Spectral Clustering and Reinforcement Learning, Applied Sciences, 2021 (DOI)
Multi-Server Multi-User Multi-Task Computation Offloading for Mobile Edge Computing Networks, Sensors, 2019 (DOI)
Optimal Computational Power Allocation in Multi-Access Mobile Edge Computing for Blockchain, Sensors, 2018 (DOI)
Optimal Resource Allocation for Uplink Data Collection in Nonorthogonal Multiple Access Networks, Sensors, 2018 (DOI)
RFID Data-Driven Vehicle Speed Prediction via Adaptive Extended Kalman Filter, Sensors, 2018 (DOI)
Non-orthogonal multiple access assisted federated learning via wireless power transfer: A cost-efficient approach, IEEE Transactions on Communications, 2022 (Cited by 133)
Multi-server multi-user multi-task computation offloading for mobile edge computing networks, Sensors, 2019 (Cited by 130)
SWIPT cooperative spectrum sharing for 6G-enabled cognitive IoT network, IEEE Internet of Things Journal, 2022