Index - Research Assistant Professor

Name

ZHANG, Zezhong

Title

Research Assistant Professor

Education Background

Postdoc. [The Future Network of Intelligence Institute (FNii), The Chinese University of

Hong Kong, Shenzhen]

Ph.D. (The University of Hong Kong)

B.Eng. (Southern University of Science and Technology)

Research

Edge learning, radio map estimation, machine learning, and B5G technologies including integrated sensing & communication (ISAC) and massive MIMO networks

Personal Website

zhangzezhong@cuhk.edu.cn

Office Address

Phone

Teaching Area

Other

Biography

Zezhong Zhang received the B.Eng. degree from Southern University of Science and Technology (SUSTech) in 2017, and the Ph.D. degree from The University of Hong Kong in 2021, both in electrical and electronic engineering. Following this, He spent two years working as a Postdoc Fellow in The Chinese University of Hong Kong, Shenzhen from 2021 to 2023. He joined The Chinese University of Hong Kong, Shenzhen as a Research Assistant Professor in the School of Science and Engineering since 2024. His research interests are in the area of edge learning, radio map estimation, machine learning, and B5G technologies including integrated sensing & communication (ISAC) and massive MIMO networks.

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Awards and honors

Publications

Z. Zhang, G. Zhu, R. Wang, V. K. N. Lau, and K. Huang, “Turning Channel Noise into an Accelerator for Over-the-Air Principal Component Analysis”, IEEE Trans. Wireless Commun., vol. 21, no. 10, pp. 7926-7941, Oct. 2022.
Z. Zhang, S. W. Ko, R. Wang, and K. Huang, “Cooperative Multi-Point Vehicular Positioning Using Millimeter-Wave Surface Reflection”, IEEE Trans. Wireless Commun., vol. 20, no. 4, pp. 2221-2236, Dec. 2020.
Z. Zhang, Y. Li, R. Wang, and K. Huang, “Rate Adaptation for Downlink Massive MIMO Networks and Underlaid D2D Links: A Learning Approach”, IEEE Trans. Wireless Commun., vol. 18, no. 3, pp. 1819-1833, Feb. 2019.
Z. Zhang, Y. Li, and R. Wang, “Suppressing Pilot Contamination in Massive MIMO Downlink via Cross-Frame Scheduling”, IEEE Access, vol. 6, pp. 44858-44867, 2018.
S. Huang, Z. Zhang*, S. Wang, R. Wang*, and K. Huang, “Accelerating Federated Edge Learning via Topology Optimization”, IEEE Internet of Things J., vol. 10, no. 3, pp. 2056-2070, Feb. 2023.

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