| Digital twin-aided learning to enable robust beamforming: Limited feedback meets deep generative models Y Li, K Li, L Cheng, Q Shi, ZQ Luo 2021 IEEE 22nd International Workshop on Signal Processing Advances in …, 2021 | 10 | 2021 |
| Learning enhanced beamforming vector from CQIs in 5G NR FDD massive MIMO systems: A tuning-free approach K Li, Y Li, L Cheng, Q Shi, ZQ Luo 2021 IEEE 22nd International Workshop on Signal Processing Advances in …, 2021 | 7 | 2021 |
| Downlink channel covariance matrix reconstruction for FDD massive MIMO systems with limited feedback K Li, Y Li, L Cheng, Q Shi, ZQ Luo IEEE Transactions on Signal Processing, 2024 | 4 | 2024 |
| Pushing the limit of Type I codebook for FDD massive MIMO beamforming: A channel covariance reconstruction approach K Li, Y Li, L Cheng, Q Shi, ZQ Luo ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 4 | 2021 |
| Towards Efficient Modeling and Inference in Multi-Dimensional Gaussian Process State-Space Models Z Lin, J Maroñas, Y Li, F Yin, S Theodoridis ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and …, 2024 | 3 | 2024 |
| Overcoming Posterior Collapse in Variational Autoencoders Via EM-Type Training Y Li, L Cheng, F Yin, MM Zhang, S Theodoridis ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and …, 2023 | 2 | 2023 |
| Enhancing Multi-Stream Beamforming Through CQIs For 5G NR FDD Massive MIMO Communications: A Tuning-Free Scheme K Li, Y Li, L Cheng, ZQ Luo IEEE Transactions on Wireless Communications, 2024 | | 2024 |
| Preventing Model Collapse in Gaussian Process Latent Variable Models Y Li, Z Lin, F Yin, MM Zhang International Conference on Machine Learning (ICML), 2024 | | 2024 |
| Online/Offline Learning to Enable Robust Beamforming: Limited Feedback Meets Deep Generative Models Y Li, Z Lin, K Li, MM Zhang arXiv preprint arXiv:2404.06055, 2024 | | 2024 |
Lei Cheng (程磊)Assistant Professor (ZJU Young Professor), Zhejiang University在 eee.hku.hk 的电子邮件经过验证
