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Cited by 375

The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

Satellite Mobile Edge Computing: Improving QoS of High-Speed Satellite-Terrestrial Networks Using Edge Computing Techniques

Zhang, Zhenjiang, Zhang, Wenyu, Tseng, Fan-Hsun·2019·IEEE Network

Abstract

The high-speed satellite-terrestrial network (STN) is an indispensable alternative in future mobile communication systems. In this article, we first introduce the architecture and application scenarios of STNs, and then investigate possible ways to implement mobile edge computing (MEC) technique for QoS improvement in STNs. We propose satellite MEC (SMEC), in which a user equipment without a proximal MEC server can also enjoy MEC services via satellite links. We propose a dynamic network virtualization technique to integrate the network resources, and furtherly design a cooperative computation offloading (CCO) model to achieve parallel computation in STNs. Task scheduling models in SMEC are discussed in detail, and an elemental simulation is conducted to evaluate the performance of the proposed CCO model in SMEC.

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Metadata

Authors
Zhang, Z., Zhang, W., & Tseng, F.-H.
Venue
IEEE Network
Published
2019-01
Volume
33
Issue
1
Pages
70-76
ISSN
  • 0890-8044 (ISSN-L)
  • 1558-156X
OpenAlex
W2909847702
Cited by
375
References
11
FWCI
30.57
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Satellite Mobile Edge Computing: Improving QoS of High-Speed Satellite-Terrestrial Networks Using Edge Computing Techniques. https://app.cochat.ai/papers/10.1109/mnet.2018.1800172
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Zhang, Z., Zhang, W., & Tseng, F.-H. (2019). Satellite Mobile Edge Computing: Improving QoS of High-Speed Satellite-Terrestrial Networks Using Edge Computing Techniques. IEEE Network. https://doi.org/10.1109/mnet.2018.1800172

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.