252 citations · 282 across the 12 of their papers we have counts for
12 papers
Deep Learning-empowered Predictive Precoder Design for OTFS Transmission in URLLC
Chang Liu, Shuangyang Li, Weijie Yuan +2
To guarantee excellent reliability performance in ultra-reliable low-latency communications (URLLC), pragmatic precoder design is an effective approach. However, an efficient preco…
Predictive Precoder Design for OTFS-Enabled URLLC: A Deep Learning Approach
Chang Liu, Shuangyang Li, Weijie Yuan +2
This paper investigates the orthogonal time frequency space (OTFS) transmission for enabling ultra-reliable low-latency communications (URLLC). To guarantee excellent reliability p…
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…
Learning-based Predictive Beamforming for Integrated Sensing and Communication in Vehicular Networks
Chang Liu, Weijie Yuan, Shuangyang Li +4
This paper investigates the integrated sensing and communication (ISAC) in vehicle-to-infrastructure (V2I) networks. To realize ISAC, an effective beamforming design is essential w…
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…
Location-aware Predictive Beamforming for UAV Communications: A Deep Learning Approach
Chang Liu, Weijie Yuan, Zhiqiang Wei +2
Unmanned aerial vehicle (UAV)-assisted communication becomes a promising technique to realize the beyond fifth generation (5G) wireless networks, due to the high mobility and maneu…
Deep Residual Learning-Assisted Channel Estimation in Ambient Backscatter Communications
Xuemeng Liu, Chang Liu, Yonghui Li +2
Channel estimation is a challenging problem for realizing efficient ambient backscatter communication (AmBC) systems. In this letter, channel estimation in AmBC is modeled as a den…
Deep Residual Learning for Channel Estimation in Intelligent Reflecting Surface-Assisted Multi-User Communications
Chang Liu, Xuemeng Liu, Derrick Wing Kwan Ng +1
Channel estimation is one of the main tasks in realizing practical intelligent reflecting surface-assisted multi-user communication (IRS-MC) systems. However, different from tradit…