P
Ping Zhang
Researcher at Beijing University of Posts and Telecommunications
Publications - 653
Citations - 8124
Ping Zhang is an academic researcher from Beijing University of Posts and Telecommunications. The author has contributed to research in topics: Computer science & Cognitive radio. The author has an hindex of 36, co-authored 493 publications receiving 5378 citations.
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Journal ArticleDOI
Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts
Xiaohu You,Cheng-Xiang Wang,Jie Huang,Xiqi Gao,Zaichen Zhang,Michael Mao Wang,Yongming Huang,Chuan Zhang,Yanxiang Jiang,Jiaheng Wang,Min Zhu,Bin Sheng,Dongming Wang,Zhiwen Pan,Pengcheng Zhu,Yang Yang,Zening Liu,Ping Zhang,Xiaofeng Tao,Shaoqian Li,Zhi Chen,Xinying Ma,Chih-Lin I,Shuangfeng Han,Ke Li,Pan Chengkang,Zhiming Zheng,Lajos Hanzo,Xuemin Shen,Yingjie Jay Guo,Zhiguo Ding,Harald Haas,Wen Tong,Peiying Zhu,Guanghua Yang,Jun Wang,Eric G. Larsson,Hien Quoc Ngo,Wei Hong,Haiming Wang,Debin Hou,Jixin Chen,Zhe Chen,Zhang-Cheng Hao,Geoffrey Ye Li,Rahim Tafazolli,Yue Gao,H. Vincent Poor,Gerhard P. Fettweis,Ying-Chang Liang +49 more
TL;DR: 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.
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Multiuser Joint Task Offloading and Resource Optimization in Proximate Clouds
TL;DR: This paper proposes a heuristic offloading decision algorithm (HODA), which is semidistributed and jointly optimizes the offload decision, and communication and computation resources to maximize system utility, a measure of quality of experience based on task completion time and energy consumption of a mobile device.
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ConFi: Convolutional Neural Networks Based Indoor Wi-Fi Localization Using Channel State Information
TL;DR: The ConFi is proposed, the first convolutional neural network (CNN)-based Wi-Fi localization algorithm that addresses localization as a classification problem and addresses it with a five layer CNN that consists of three convolutionAL layers and two fully connected layers.
Journal ArticleDOI
Energy-Efficient Admission of Delay-Sensitive Tasks for Mobile Edge Computing
TL;DR: Simulations show that this approach is able to dramatically enhance the scalability of task admission at a marginal cost of extra energy, as compared with the optimal branch and bound method, and can be efficiently implemented for online programming.
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Cooperative Task Offloading in Three-Tier Mobile Computing Networks: An ADMM Framework
TL;DR: This paper considers a cooperative three-tier computing network by leveraging the vertical cooperation among devices, edge nodes and cloud servers, as well as the horizontal cooperation between edge nodes to develop an efficient offloading scheme with low complexity.