6 citations · 24 across the 8 of their papers we have counts for
8 papers
Scalable Predictive Beamforming for IRS-Assisted Multi-User Communications: A Deep Learning Approach
Chang Liu, Xuemeng Liu, Zhiqiang Wei +2
Beamforming design for intelligent reflecting surface (IRS)-assisted multi-user communication (IRS-MUC) systems critically depends on the acquisition of accurate channel state info…
Deep CLSTM for Predictive Beamforming in Integrated Sensing and Communication-enabled Vehicular Networks
Chang Liu, Xuemeng Liu, Shuangyang Li +2
Predictive beamforming design is an essential task in realizing high-mobility integrated sensing and communication (ISAC), which highly depends on the accuracy of the channel predi…
Predictive Beamforming for Integrated Sensing and Communication in Vehicular Networks: A Deep Learning Approach
Chang Liu, Weijie Yuan, Shuangyang Li +3
The implementation of integrated sensing and communication (ISAC) highly depends on the effective beamforming design exploiting accurate instantaneous channel state information (IC…
Deep Learning-Empowered Predictive Beamforming for IRS-Assisted Multi-User Communications
Chang Liu, Xuemeng Liu, Zhiqiang Wei +3
The realization of practical intelligent reflecting surface (IRS)-assisted multi-user communication (IRS-MUC) systems critically depends on the proper beamforming design exploiting…
Deep Residual Network Empowered Channel Estimation for IRS-Assisted Multi-User Communication Systems
Chang Liu, Xuemeng Liu, Derrick Wing Kwan Ng +1
Channel estimation is of great importance in realizing practical intelligent reflecting surface-assisted multi-user communication (IRS-MC) systems. However, different from traditio…
Deep Transfer Learning-Assisted Signal Detection for Ambient Backscatter Communications
Chang Liu, Xuemeng Liu, Zhiqiang Wei +3
Existing tag signal detection algorithms inevitably suffer from a high bit error rate (BER) due to the difficulties in estimating the channel state information (CSI). To eliminate…