M
Min Sheng
Researcher at Xidian University
Publications - 337
Citations - 6819
Min Sheng is an academic researcher from Xidian University. The author has contributed to research in topics: Wireless network & Throughput. The author has an hindex of 33, co-authored 309 publications receiving 4670 citations. Previous affiliations of Min Sheng include Cornell University.
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Mobile-Edge Computing: Partial Computation Offloading Using Dynamic Voltage Scaling
TL;DR: This paper investigates partial computation offloading by jointly optimizing the computational speed of smart mobile device (SMD), transmit power of SMD, and offloading ratio with two system design objectives: energy consumption of ECM minimization and latency of application execution minimization.
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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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When UAV Meets IRS: Expanding Air-Ground Networks via Passive Reflection
TL;DR: In this article, an intelligent reflecting surface (IRS) is employed to enhance the performance of UAV-aided air-ground networks, where the UAV trajectory, the transmit beamforming and the RIS passive beamforming are jointly optimized.
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D2D Enhanced Heterogeneous Cellular Networks With Dynamic TDD
TL;DR: This work studies a two-tier heterogeneous cellular network where the macro tier and small cell tier operate according to a dynamic TDD scheme on orthogonal frequency bands and provides guidelines for the optimal design of D2D network access.
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Learning-Based Content Caching and Sharing for Wireless Networks
TL;DR: This paper proposes a centralized algorithm by employing a semidefinite relaxation approach, and proves that this centralized algorithm learns efficient caching by deriving a sub-linear learning regret bound, and proposes a distributed algorithm based on alternating direction method of multipliers, where each BS only solves their own problems by exchanging local information with neighbor BSs.