S
Sheng Zhou
Researcher at Tsinghua University
Publications - 361
Citations - 9007
Sheng Zhou is an academic researcher from Tsinghua University. The author has contributed to research in topics: Base station & Scheduling (computing). The author has an hindex of 48, co-authored 339 publications receiving 6936 citations. Previous affiliations of Sheng Zhou include Hitachi.
Papers
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Journal ArticleDOI
EMM: Energy-Aware Mobility Management for Mobile Edge Computing in Ultra Dense Networks
Yuxuan Sun,Sheng Zhou,Jie Xu +2 more
TL;DR: In this article, a user-centric energy-aware mobility management (EMM) scheme is proposed to optimize the delay due to both radio access and computation under the long-term energy consumption constraint of the user.
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Computation Peer Offloading for Energy-Constrained Mobile Edge Computing in Small-Cell Networks
Lixing Chen,Sheng Zhou,Jie Xu +2 more
TL;DR: In this article, a peer offloading game among small-cell base stations (SBSs) is proposed to maximize the long-term system performance while keeping the energy consumption of SBSs below individual longterm constraints.
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Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning
TL;DR: In this paper, a joint device scheduling and resource allocation policy is proposed to maximize the model accuracy within a given total training time budget for latency constrained wireless FL, where a lower bound on the reciprocal of the training performance loss is derived.
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Spatial modeling of the traffic density in cellular networks
TL;DR: This article presents the analysis of traffic measurements collected from commercial cellular networks in China, and demonstrates that the spatial distribution of the traffic density can be approximated by the log-normal or Weibull distribution depending on time and space.
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Water-Filling: A Geometric Approach and its Application to Solve Generalized Radio Resource Allocation Problems
TL;DR: A simple and elegant geometric water-filling approach to solve the unweighted and weighted radio resource allocation problems, which eliminates the step to find the water level through solving a non-linear system from the Karush-Kuhn-Tucker conditions of the target problem.