5G-Enabled UAV-to-Community Offloading: Joint Trajectory Design and Task Scheduling

Zhaolong Ning, Peiran Dong, Miaowen Wen, Xiaojie Wang, Lei Guo, Ricky Y.K. Kwok, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

68 Scopus citations


Due to line-of-sight communication links and distributed deployment, Unmanned Aerial Vehicles (UAVs) have attracted substantial interest in agile Mobile Edge Computing (MEC) service provision. In this paper, by clustering multiple users into independent communities based on their geographic locations, we design a 5G-enabled UAV-to-community offloading system. A system throughput maximization problem is formulated, subjected to the transmission rate, atomicity of tasks and speed of UAVs. By relaxing the transmission rate constraint, the mixed integer non-linear program is transformed into two subproblems. We first develop an average throughput maximization-based auction algorithm to determine the trajectory of UAVs, where a community-based latency approximation algorithm is developed to regulate the designed auction bidding. Then, a dynamic task admission algorithm is proposed to solve the task scheduling subproblem within one community. Performance analyses demonstrate that our designed auction bidding can guarantee user truthfulness, and can be fulfilled in polynomial time. Extensive simulations based on real-world data in health monitoring and online YouTube video services show that our proposed algorithm is able to maximize the system throughput while guaranteeing the fraction of served users.

Original languageEnglish (US)
Pages (from-to)3306-3320
Number of pages15
JournalIEEE Journal on Selected Areas in Communications
Issue number11
StatePublished - Nov 1 2021

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Electrical and Electronic Engineering


  • 5G communications
  • UAV
  • mobile edge computing
  • task scheduling
  • trajectory design


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