6 citations · 12 across the 13 of their papers we have counts for
13 papers
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…
Efficient Algorithm Design of Dynamic Spectrum Access by Whittle Index
Keqin Liu, Qizhen Jia, Yiying Zhang +1
Dynamic spectrum access problem is an important problem that allows a wireless sub-network to use channels temporarily unoccupied by the parent network for minimizing the spectrum…
Over-the-Air Federated Learning in MIMO Cloud-RAN Systems
Haoming Ma, Xiaojun Yuan, Zhi Ding
To address the limitations of traditional over-the-air federated learning (OA-FL) such as limited server coverage and low resource utilization, we propose an OA-FL in MIMO cloud ra…
Low-Complexity Memory AMP Detector for High-Mobility MIMO-OTFS SCMA Systems
Yao Ge, Lei Liu, Shunqi Huang +3
Efficient signal detectors are rather important yet challenging to achieve satisfactory performance for large-scale communication systems. This paper considers a non-orthogonal spa…
OTFS Signaling for SCMA With Coordinated Multi-Point Vehicle Communications
Yao Ge, Qinwen Deng, David González G. +2
This paper investigates an uplink coordinated multi-point (CoMP) coverage scenario, in which multiple mobile users are grouped for sparse code multiple access (SCMA), and served by…
Convergence Acceleration in Wireless Federated Learning: A Stackelberg Game Approach
Kaidi Wang, Yi Ma, Mahdi Boloursaz Mashhadi +3
This paper studies issues that arise with respect to the joint optimization for convergence time in federated learning over wireless networks (FLOWN). We consider the criterion and…
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…
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…
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…
Over-the-Air Federated Multi-Task Learning
Haoming Ma, Xiaojun Yuan, Dian Fan +3
In this letter, we introduce over-the-air computation into the communication design of federated multi-task learning (FMTL), and propose an over-the-air federated multi-task learni…