9 citations · 9 across the 5 of their papers we have counts for
5 papers
SPA-MAE: A Physics-Guided CSI Foundation Model for Wireless Physical Layer
Chen Chen, Weijie Jin, Hengtao He +2
Deep learning (DL) has been widely used in future 6G physical layer communications, but task-specific DL models are difficult to generalize across different physical layer tasks. R…
Deep Learning-based Low-Overhead Beam Alignment for mmWave Massive MIMO Systems
Weijie Jin, Jing Zhang, Hengtao He +3
Millimeter-wave massive multiple-input multiple-output systems employ highly directional beamforming to overcome severe path loss, and their performance critically depends on accur…
Low-Complexity Joint Beamforming for RIS-Assisted MU-MISO Systems Based on Model-Driven Deep Learning
Weijie Jin, Jing Zhang, Chao-Kai Wen +3
Reconfigurable intelligent surfaces (RIS) can improve signal propagation environments by adjusting the phase of the incident signal. However, optimizing the phase shifts jointly wi…
Adaptive Channel Estimation Based on Model-Driven Deep Learning for Wideband mmWave Systems
Weijie Jin, Hengtao He, Chao-Kai Wen +2
Channel estimation in wideband millimeter-wave (mmWave) systems is very challenging due to the beam squint effect. To solve the problem, we propose a learnable iterative shrinkage…
Beamspace Channel Estimation for Wideband Millimeter-Wave MIMO: A Model-Driven Unsupervised Learning Approach
Hengtao He, Rui Wang, Weijie Jin +3
Millimeter-wave (mmWave) communications have been one of the promising technologies for future wireless networks that integrate a wide range of data-demanding applications. To comp…