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Yejun He
Researcher at Shenzhen University
Publications - 239
Citations - 5323
Yejun He is an academic researcher from Shenzhen University. The author has contributed to research in topics: Antenna (radio) & Dipole antenna. The author has an hindex of 28, co-authored 208 publications receiving 3604 citations. Previous affiliations of Yejun He include Georgia Institute of Technology & The Chinese University of Hong Kong.
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Mobile-Edge Computing for Vehicular Networks: A Promising Network Paradigm with Predictive Off-Loading
TL;DR: A cloud-based mobileedge computing (MEC) off-loading framework in vehicular networks is proposed, where the tasks are adaptively off-loaded to the MEC servers through direct uploading or predictive relay transmissions, which greatly reduces the cost of computation and improves task transmission efficiency.
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Enabling device-to-device communications in millimeter-wave 5G cellular networks
TL;DR: This article introduces an mmWave+4G system architecture with TDMA-based MAC structure as a candidate for 5G cellular networks and proposes an effective resource sharing scheme by allowing non-interfering D2D links to operate concurrently.
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Deep Learning Empowered Task Offloading for Mobile Edge Computing in Urban Informatics
TL;DR: This work adopts a deep Q-learning approach for designing optimal offloading schemes and proposes an efficient redundant offloading algorithm to improve task offloading reliability in the case of vehicular data transmission failure and evaluates the proposed schemes based on real traffic data.
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Mobile Edge Computing and Networking for Green and Low-Latency Internet of Things
TL;DR: This article presents a mobility-aware hierarchical MEC framework for green and low-latency IoT, and deploys a game theoretic approach for computation offloading in order to optimize the utility of the service providers while also reducing the energy cost and the task execution time of the smart devices.
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A Non-Stationary 3-D Wideband Twin-Cluster Model for 5G Massive MIMO Channels
TL;DR: Numerical analysis shows that the proposed channel models are able to serve as a design framework for massive MIMO channel modeling.