Pushing The Limit of Type I Codebook For Fdd Massive Mimo Beamforming: A Channel Covariance Reconstruction Approach. - Kai Li 031; Ying Li 047; Lei Cheng 003; Qingjiang Shi; Zhi-Quan Luo (ICASSP, 2021)

Learning Enhanced Beamforming Vector From CQIs in 5G NR FDD Massive MIMO Systems: A Tuning-free Approach. - Kai Li 031; Ying Li 047; Lei Cheng 003; Qingjiang Shi; Zhi-Quan Luo (SPAWC, 2021)

Digital Twin-Aided Learning to Enable Robust Beamforming: Limited Feedback Meets Deep Generative Models. - Ying Li 047; Kai Li 031; Lei Cheng 003; Qingjiang Shi; Zhi-Quan Luo (SPAWC, 2021)

Overcoming Posterior Collapse in Variational Autoencoders Via EM-Type Training. - Ying Li 047; Lei Cheng 003; Feng Yin; Michael Minyi Zhang; Sergios Theodoridis (ICASSP, 2023)

Towards Efficient Modeling and Inference in Multi-Dimensional Gaussian Process State-Space Models. - Zhidi Lin; Juan Maroñas; Ying Li 047; Feng Yin; Sergios Theodoridis (ICASSP, 2024)

Downlink Channel Covariance Matrix Reconstruction for FDD Massive MIMO Systems With Limited Feedback. - Kai Li 031; Ying Li 047; Lei Cheng 003; Qingjiang Shi; Zhi-Quan Luo (IEEE Trans. Signal Process., 2024)

Enhancing Multi-Stream Beamforming Through CQIs for 5G NR FDD Massive MIMO Communications: A Tuning-Free Scheme. - Kai Li 031; Ying Li 047; Lei Cheng 003; Zhi-Quan Luo (IEEE Trans. Wirel. Commun., 2024)

Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems. - Zhidi Lin; Ying Li 047; Feng Yin; Juan Maroñas; Alexandre H. Thiéry (CoRR, 2025)