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UAV-Assisted MEC with an Expandable Computing Resource Pool: Rethinking the UAV Deployment

  • Yiqin DENG
  • , Haixia ZHANG
  • , Xianhao CHEN
  • , Yuguang FANG

Research output: Journal PublicationsJournal Article (refereed)peer-review

Abstract

Unmanned aerial vehicles (UAVs) have emerged as a promising solution to establish links to reach edge servers (ESs) in scenarios where ground users (GUs) lack direct connections. How-ever, a predominant focus in existing research on UAV deployment centers around optimizing system performance under the consideration of a fixed computing resource pool, resulting in a potential computing bottleneck for delivering large-scale multi-access edge computing (MEC) services. In this article, we present an innovative approach, aiming at enhancing MEC performance by expanding the computing resource pool in a UAV-assisted system through effective management of UAV altitude and mobility. With this idea, the joint design of communications and computing needs to be reconsidered. Specifically, we briefly overview the problems when leveraging UAVs to coordinate communication and computing resources, and review closely related work on UAV-assisted MEC systems. Then, we elaborate on the proposed service network architecture, the modeling, and related optimization problems to boost the utilization of resources. Finally, we utilize a use case to demonstrate the effectiveness of our design approach and point out several research directions.
Original languageEnglish
Pages (from-to)110-116
Number of pages7
JournalIEEE Wireless Communications
Volume31
Issue number5
Early online date21 May 2024
DOIs
Publication statusPublished - Oct 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Funding

The work of Yiqin Deng was supported in part by the National Natural Science Foundation of China under Grant No. 62301300, in part by the China Postdoctoral Science Foundation under Grant No. 2023M732090, in part by the Shandong Province Science Foundation under Grant No. ZR2023QF053 and the work of Haixia Zhang was supported in part by the Joint Funds of the NSFC under Grant No. U22A2003. The work of Xianhao Chen was supported in part by HKU IDS Research Seed Fund under Grant IDS-RSF2023-0012. The work of Y. Fang was supported in part by the Hong Kong SAR Government under the Global STEM Professorship and the Hong Kong Jockey Club under JC STEM Lab of Smart City.

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