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Nan Zhao
Researcher at Dalian University of Technology
Publications - 364
Citations - 12000
Nan Zhao is an academic researcher from Dalian University of Technology. The author has contributed to research in topics: Computer science & Wireless network. The author has an hindex of 47, co-authored 286 publications receiving 7685 citations. Previous affiliations of Nan Zhao include Anhui University of Technology & Harbin Institute of Technology.
Papers
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A Survey of Channel Modeling for UAV Communications
TL;DR: An extensive survey of the measurement methods proposed for UAV channel modeling that use low altitude platforms and discusses various channel characterization efforts is provided.
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Integrated Networking, Caching, and Computing for Connected Vehicles: A Deep Reinforcement Learning Approach
Ying He,Nan Zhao,Hongxi Yin +2 more
TL;DR: This paper proposes an integrated framework that can enable dynamic orchestration of networking, caching, and computing resources to improve the performance of next generation vehicular networks and formulate the resource allocation strategy in this framework as a joint optimization problem.
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UAV-Assisted Emergency Networks in Disasters
TL;DR: A unified framework for a UAV-assisted emergency network is established in disasters by jointly optimized to provide wireless service to ground devices with surviving BSs and multihop UAV relaying to realize information exchange between the disaster areas and outside through optimizing the hovering positions of UAVs.
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Software-Defined Networks with Mobile Edge Computing and Caching for Smart Cities: A Big Data Deep Reinforcement Learning Approach
TL;DR: An integrated framework that can enable dynamic orchestration of networking, caching, and computing resources to improve the performance of applications for smart cities is proposed and a novel big data deep reinforcement learning approach is presented.
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UAV Trajectory Optimization for Data Offloading at the Edge of Multiple Cells
TL;DR: In this correspondence, the sum rate of UAV-served edge users is maximized subject to the rate requirements for all the users, by optimizing the UAV trajectory in each flying cycle to offload traffic for BSs.