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Kezhi Wang

Researcher at Northumbria University

Publications -  213
Citations -  8175

Kezhi Wang is an academic researcher from Northumbria University. The author has contributed to research in topics: Computer science & Mobile edge computing. The author has an hindex of 28, co-authored 175 publications receiving 3469 citations. Previous affiliations of Kezhi Wang include Beijing Normal University & Central South University.

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Journal ArticleDOI

Task-Load-Aware Game-Theoretic Framework for Wireless Federated Learning

TL;DR: A Bertrand-game based framework to address the incentive problem, where a model owner issues an FL task and the employed UEs help train the model by using their local data, and obtains the Nash equilibrium of the game in closed form.
Journal ArticleDOI

Computation Efficiency Optimization for Millimeter-Wave Mobile Edge Computing Networks With NOMA

TL;DR: In this paper , the authors studied the joint optimization of the analog beamforming (ABF) and hybrid beamforming architectures under the partial offloading mode in mmWave-MEC with NOMA.
Proceedings ArticleDOI

Average Data Rate and Decoding Error Probability Analysis for IRS-aided URLLC in a Factory Automation Scenario

TL;DR: In this paper, the authors considered a factory automation (FA) scenario, where wireless signal are easy to be blocked by densely deployed machines and proposed an intelligent reflecting surface (IRS) to create an alternative transmission link when the direct link is blocked, which can enhance the transmission reliability.
Journal ArticleDOI

Combining Lyapunov Optimization With Evolutionary Transfer Optimization for Long-Term Energy Minimization in IRS-Aided Communications

TL;DR: This article studies an intelligent reflecting surface (IRS)-aided communication system under the time-varying channels and stochastic data arrivals to optimize the phase-shift coefficient and the transmit power in sequential time slots to maximize the long-term energy consumption for all mobile devices while ensuring queue stability.
Posted Content

UAV-Assisted and Intelligent Reflecting Surfaces-Supported Terahertz Communications

TL;DR: In this paper, the joint optimization of UAV's trajectory, the phase shift of RIS, the allocation of THz subbands, and the power control is investigated to maximize the minimum average achievable rate of all the users.