5 citations · 5 across the 6 of their papers we have counts for
6 papers
Low-Complexity CSI Feedback for FDD Massive MIMO Systems via Learning to Optimize
Yifan Ma, Hengtao He, Shenghui Song +2
In frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) systems, the growing number of base station antennas leads to prohibitive feedback overhead for dow…
Lightweight and Flexible Deep Equilibrium Learning for CSI Feedback in FDD Massive MIMO
Yifan Ma, Wentao Yu, Xianghao Yu +3
In frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems, downlink channel state information (CSI) needs to be sent back to the base station (BS)…
Augmented Deep Unfolding for Downlink Beamforming in Multi-cell Massive MIMO With Limited Feedback
Yifan Ma, Xianghao Yu, Jun Zhang +2
In limited feedback multi-user multiple-input multiple-output (MU-MIMO) cellular networks, users send quantized information about the channel conditions to the associated base stat…
Learn to Communicate with Neural Calibration: Scalability and Generalization
Yifan Ma, Yifei Shen, Xianghao Yu +3
The conventional design of wireless communication systems typically relies on established mathematical models that capture the characteristics of different communication modules. U…
Neural Calibration for Scalable Beamforming in FDD Massive MIMO with Implicit Channel Estimation
Yifan Ma, Yifei Shen, Xianghao Yu +3
Channel estimation and beamforming play critical roles in frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. However, these two modules have…
A Low-Complexity Algorithmic Framework for Large-Scale IRS-Assisted Wireless Systems
Yifan Ma, Yifei Shen, Xianghao Yu +3
Intelligent reflecting surfaces (IRSs) are revolutionary enablers for next-generation wireless communication networks, with the ability to customize the radio propagation environme…