| 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 | 5 | 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 |
| 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 |
| 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 |
| An Exploration-Estimation Beamforming Scheme For 5GNR FDD Massive MIMO Communications K Li, W Pu, ZQ Luo 2023 IEEE 24th International Workshop on Signal Processing Advances in …, 2023 | | 2023 |
Zhi-Quan LuoProfessor, The Chinese University of Hong Kong, Shenzhen, China在 cuhk.edu.cn 的电子邮件经过验证
