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20062023
most citedDistributed Compressive CSIT Estimation and Feedback for FDD Multi-user Massive MIMO Systems

722 citations · 4.3k across the 116 of their papers we have counts for

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Showing 2021Show all

6 papers · 1 filter

eess.SP2021★ 151 cited

Cascaded Channel Estimation for Intelligent Reflecting Surface Assisted Multiuser MISO Systems

Huayan Guo, Vincent K. N. Lau

This paper investigates the uplink cascaded channel estimation for intelligent-reflecting-surface (IRS)-assisted multi-user multiple-input-single-output systems. We focus on a sub-…

cs.IT2021★ 1 cited

Dynamic RAT Selection and Transceiver Optimization for Mobile Edge Computing Over Multi-RAT Heterogeneous Networks

Feng Wang, Vincent K. N. Lau

Mobile edge computing (MEC) integrated with multiple radio access technologies (RATs) is a promising technique for satisfying the growing low-latency computation demand of emerging…

cs.IT2021★ 2 cited

Multi-Level Over-the-Air Aggregation of Mobile Edge Computing over D2D Wireless Networks

Feng Wang, Vincent K. N. Lau

In this paper, we consider a wireless multihop device-to-device (D2D) based mobile edge computing (MEC) system, where the destination wireless device (WD) is scheduled to compute n…

math.OC2021★ 8 cited

Online Optimal State Feedback Control of Linear Systems over Wireless MIMO Fading Channels

Songfu Cai, Vincent Lau

We consider the optimal control of linear systems over wireless MIMO fading channels, where the MIMO wireless fading and random access of the remote controller may cause intermitte…

cs.IT2021

Turning Channel Noise into an Accelerator for Over-the-Air Principal Component Analysis

Zezhong Zhang, Guangxu Zhu, Rui Wang +2

Recently years, the attempts on distilling mobile data into useful knowledge has been led to the deployment of machine learning algorithms at the network edge. Principal component…

cs.IT2021★ 3 cited

Deploying Federated Learning in Large-Scale Cellular Networks: Spatial Convergence Analysis

Zhenyi Lin, Xiaoyang Li, Vincent K. N. Lau +2

The deployment of federated learning in a wireless network, called federated edge learning (FEEL), exploits low-latency access to distributed mobile data to efficiently train an AI…