1 citations · 2 across the 8 of their papers we have counts for
6 papers · 1 filter
Structured-Sparsity-Aware Joint User Activity Detection and Channel Estimation for OTFS-Based Grant-Free Random Access
Yao Ge, Yirui Luo, Yuhao Chi +4
Grant-free random access (GFRA) is a promising solution for massive machine-type communications (mMTC) in future wireless networks. However, reliable user activity detection and ch…
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…
Over-the-Air Federated Multi-Task Learning via Model Sparsification and Turbo Compressed Sensing
Haoming Ma, Xiaojun Yuan, Zhi Ding +2
To achieve communication-efficient federated multitask learning (FMTL), we propose an over-the-air FMTL (OAFMTL) framework, where multiple learning tasks deployed on edge devices s…
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…
Learning-Based MIMO Channel Estimation under Spectrum Efficient Pilot Allocation and Feedback
Mason del Rosario, Zhi Ding
Wireless links using massive MIMO transceivers are vital for next generation wireless communications networks networks. Precoding in Massive MIMO transmission requires accurate dow…
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…