35 citations · 52 across the 7 of their papers we have counts for
8 papers
Zeroth-Order Blind Interference Suppression for Multi-RIS-Aided Wireless Systems
Binyao Ma, Peilan Wang, Bin Wang +1
In this paper, we study measurement-driven signal-to-interference-plus-noise ratio (SINR) maximization for a multi-reconfigurable intelligent surface (RIS)-aided single-input singl…
Max-Min Beamforming for Large-Scale Cell-Free Massive MIMO: A Randomized ADMM Algorithm
Bin Wang, Jun Fang, Yue Xiao +1
We consider the problem of max-min beamforming (MMB) for cell-free massive multi-input multi-output (MIMO) systems, where the objective is to maximize the minimum achievable rate a…
Jamming-Resistant AAV Communications: A Multichannel-Aided Approach
Bin Wang, Jun Fang, Jieru Du +1
Jamming cancellation is essential to reliable unmanned autonomous vehicle (AAV) communications in the presence of malicious jammers. In this paper, we develop a practical multichan…
Derivative-Free Optimization-Empowered Wireless Channel Reconfiguration for 6G
Peilan Wang, Jun Fang, Xianlong Zeng +3
Reconfigurable antennas, including reconfigurable intelligent surface (RIS), movable antenna (MA), fluid antenna (FA), and other advanced antenna techniques, have been studied exte…
CSI-Free Position Optimization for Movable Antenna Communication Systems: A Black-Box Optimization Approach
Xianlong Zeng, Jun Fang, Bin Wang +2
Movable antenna (MA) is a new technology which leverages local movement of antennas to improve channel qualities and enhance the communication performance. Nevertheless, to fully r…
Communication Efficient ConFederated Learning: An Event-Triggered SAGA Approach
Bin Wang, Jun Fang, Hongbin Li +1
Federated learning (FL) is a machine learning paradigm that targets model training without gathering the local data dispersed over various data sources. Standard FL, which employs…
Confederated Learning: Federated Learning with Decentralized Edge Servers
Bin Wang, Jun Fang, Hongbin Li +2
Federated learning (FL) is an emerging machine learning paradigm that allows to accomplish model training without aggregating data at a central server. Most studies on FL consider…
Recent Advances on Sub-Nyquist Sampling-Based Wideband Spectrum Sensing
Jun Fang, Bin Wang, Hongbin Li +1
Cognitive radio (CR) is a promising technology enabling efficient utilization of the spectrum resource for future wireless systems. As future CR networks are envisioned to operate…