8 citations · 18 across the 7 of their papers we have counts for
6 papers · 1 filter
Training Enhancement of Deep Learning Models for Massive MIMO CSI Feedback with Small Datasets
Zhenyu Liu, Zhi Ding
Accurate downlink channel state information (CSI) is vital to achieving high spectrum efficiency in massive MIMO systems. Existing works on the deep learning (DL) model for CSI fee…
Age of Information-based Scheduling for Wireless D2D Systems with a Deep Learning Approach
Ling Luo, Zhenyu Liu, Zhiyong Chen +3
Device-to-device (D2D) links scheduling for avoiding excessive interference is critical to the success of wireless D2D communications. Most of the traditional scheduling schemes on…
Learning-Based Phase Compression and Quantization for Massive MIMO CSI Feedback with Magnitude-Aided Information
Yu-Chien Lin, Zhenyu Liu, Ta-Sung Lee +1
Massive MIMO wireless FDD systems are often confronted by the challenge to efficiently obtain downlink channel state information (CSI). Previous works have demonstrated the potenti…
Peregrine: Network Localization and Navigation with Scalable Inference and Efficient Operation
Bryan Teague, Zhenyu Liu, Florian Meyer +2
Location-aware networks will enable new services and applications in fields such as autonomous driving, smart cities, and the Internet-of-Things. One promising solution for ubiquit…
An Efficient Deep Learning Framework for Low Rate Massive MIMO CSI Reporting
Zhenyu Liu, Lin Zhang, Zhi Ding
Channel state information (CSI) reporting is important for multiple-input multiple-output (MIMO) transmitters to achieve high capacity and energy efficiency in frequency division d…
Overcoming the Channel Estimation Barrier in Massive MIMO Communication Systems
Zhenyu Liu, Lin Zhang, Zhi Ding
A new wave of wireless services, including virtual reality, autonomous driving and internet of things, is driving the design of new generations of wireless systems to deliver ultra…