Publications (6)
Physics-Inspired Deep Learning Anti-Aliasing Framework in Efficient Channel State Feedback
Yu-Chien Lin, Yan Xin, Ta-Sung Lee +3
Acquiring downlink channel state information (CSI) at the base station is vital for optimizing performance in massive Multiple input multiple output (MIMO) Frequency-Division Duple…
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…
Deep Learning for Partial MIMO CSI Feedback by Exploiting Channel Temporal Correlation
Yu-Chien Lin, Ta-Sung Lee, Zhi Ding
Accurate estimation of DL CSI is required to achieve high spectrum and energy efficiency in massive MIMO systems. Previous works have developed learning-based CSI feedback framewor…
Exploiting Partial FDD Reciprocity for Beam Based Pilot Precoding and CSI Feedback in Deep Learning
Yu-Chien Lin, Ta-Sung Lee, Zhi Ding
Massive MIMO systems can achieve high spectrum and energy efficiency in downlink (DL) based on accurate estimate of channel state information (CSI). Existing works have developed l…
Plug-in UL-CSI-Assisted Precoder Upsampling Approach in Cellular FDD Systems
Yu-Chien Lin, Yan Xin, Ta-Sung Lee +4
Acquiring downlink channel state information (CSI) is crucial for optimizing performance in massive Multiple Input Multiple Output (MIMO) systems operating under Frequency-Division…
A Scalable Deep Learning Framework for Multi-rate CSI Feedback under Variable Antenna Ports
Yu-Chien Lin, Ta-Sung Lee, Zhi Ding
Channel state information (CSI) at transmitter is crucial for massive MIMO downlink systems to achieve high spectrum and energy efficiency. Existing works have provided deep learni…