2.3k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Shuguang Cui5 profiles29 · h 63
- Arnulf Jentzen22 · h 49
- Z. Luo18 · h 89
- Guangxu Zhu5 profiles17 · h 33
- Xiaoguang Han2 profiles15 · h 42
- Hang Li2 profiles13 · h 18
- Tsung-Hui Chang11 · h 45
- Qingjiang Shi3 profiles9 · h 36
- H. Poor2 profiles7 · h 141
- Rui Zhang3 profiles7 · h 137
- Shiyin Li7 · h 21
- Xiaoyang Li2 profiles7 · h 9
- Chinese University of Hong Kong, ShenzhenCN158 papers
- University of MünsterDE21 papers
- Chinese University of Hong KongHK16 papers
- University of Hong KongHK15 papers
- ETH ZurichCH14 papers
- Sun Yat-sen UniversityCN13 papers
- National University of SingaporeSG12 papers
- Peng Cheng LaboratoryCN10 papers
- Princeton UniversityUS10 papers
- Tongji UniversityCN10 papers
- Beijing University of Posts and TelecommunicationsCN9 papers
- Chinese Academy of SciencesCN9 papers
4 papers · 2 filters
Multi-Functional Reconfigurable Intelligent Surface: System Modeling and Performance Optimization
Wen Wang, Wanli Ni, Hui Tian +2
In this paper, we propose and study a multi-functional reconfigurable intelligent surface (MF-RIS) architecture. In contrast to conventional single-functional RIS (SF-RIS) that onl…
Successive Pose Estimation and Beam Tracking for mmWave Vehicular Communication Systems
Cen Liu, Guangxu Zhu, Fan Liu +2
The millimeter wave (mmWave) radar sensing-aided communications in vehicular mobile communication systems is investigated. To alleviate the beam training overhead under high mobili…
Energy Beamforming for RF Wireless Power Transfer with Dynamic Metasurface Antennas
Amirhossein Azarbahram, Onel L. A. Lopez, Richard D. Souza +2
Radio frequency (RF) wireless power transfer (WPT) is a promising technology for charging the Internet of Things. Practical RF-WPT systems usually require energy beamforming (EB),…
Riemannian Low-Rank Model Compression for Federated Learning with Over-the-Air Aggregation
Ye Xue, Vincent Lau
Low-rank model compression is a widely used technique for reducing the computational load when training machine learning models. However, existing methods often rely on relaxing th…