activity
20172021
most citedMISO Wireless Communication Systems via Intelligent Reflecting Surfaces

206 citations · 297 across the 19 of their papers we have counts for

collaborators

27 papers

cs.IT20212 cited

Resource Allocation for Simultaneous Wireless Information and Power Transfer Systems: A Tutorial Overview

Zhiqiang Wei, Xianghao Yu, Derrick Wing Kwan Ng +1

Over the last decade, simultaneous wireless information and power transfer (SWIPT) has become a practical and promising solution for connecting and recharging battery-limited devic…

eess.SP2021

Learn to Communicate with Neural Calibration: Scalability and Generalization

Yifan Ma, Yifei Shen, Xianghao Yu +3

The conventional design of wireless communication systems typically relies on established mathematical models that capture the characteristics of different communication modules. U…

cs.IT20211 cited

Distributed Expectation Propagation Detection for Cell-Free Massive MIMO

Hengtao He, Hanqing Wang, Xianghao Yu +3

In cell-free massive MIMO networks, an efficient distributed detection algorithm is of significant importance. In this paper, we propose a distributed expectation propagation (EP)…

eess.SP20215 cited

Neural Calibration for Scalable Beamforming in FDD Massive MIMO with Implicit Channel Estimation

Yifan Ma, Yifei Shen, Xianghao Yu +3

Channel estimation and beamforming play critical roles in frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. However, these two modules have…

cs.IT20212 cited

Channel Estimation for IRS-Assisted Millimeter-Wave MIMO Systems: Sparsity-Inspired Approaches

Tian Lin, Xianghao Yu, Yu Zhu +1

Due to their ability to create favorable line-of-sight (LoS) propagation environments, intelligent reflecting surfaces (IRSs) are regarded as promising enablers for future millimet…

cs.LG202112 cited

Communication-Efficient Federated Learning with Dual-Side Low-Rank Compression

Zhefeng Qiao, Xianghao Yu, Jun Zhang +1

Federated learning (FL) is a promising and powerful approach for training deep learning models without sharing the raw data of clients. During the training process of FL, the centr…