Residual Energy Maximization for Wireless Powered Mobile Edge Computing Systems With Mixed-Offloading

Mengru Wu, Weijing Qi, Junhee Park, Peng Lin, Lei Guo, Inkyu Lee

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


This paper studies a joint design of resource allocation and task offloading in a wireless powered mobile edge computing network involving different types of computation tasks. To deal with diverse computation tasks, we explore a mixed-offloading paradigm to support the coexistence of partial and binary offloading modes. Specifically, devices harvest energy from an access point (AP) via wireless power transfer (WPT) and utilize the harvested energy to execute their computation tasks using partial or binary offloading. Based on a practical non-linear energy harvesting model, a residual energy maximization problem is formulated by jointly optimizing the transmit power of the AP, the offloading power of devices, the time allocation on WPT and task offloading, and the task partitions and the binary offloading decisions of devices, which turn out to be a non-convex mixed-integer non-linear programming problem. Thus, we develop an efficient dual-layer optimization algorithm by decomposing the optimization problem into an inner and outer layer structure that aims to obtain resource allocation and offloading decisions. Simulation results show that our proposed scheme achieves residual energy gains compared to existing schemes.

Original languageEnglish
Pages (from-to)4523-4528
Number of pages6
JournalIEEE Transactions on Vehicular Technology
Issue number4
Publication statusPublished - 2022 Apr 1


  • Binary offloading
  • mobile edge computing
  • partial offloading
  • resource allocation
  • wireless power transfer

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics


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