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Joint Offloading and Energy Harvesting Design in Multiple Time Blocks for FDMA Based Wireless Powered MEC

Author

Listed:
  • Zhiyan Yu

    (Department of Computer Science and Technology, Jilin University, Changchun 130012, China)

  • Gaochao Xu

    (Department of Computer Science and Technology, Jilin University, Changchun 130012, China)

  • Yang Li

    (Department of Computer Science and Technology, North China University of Technology, Beijing 100144, China)

  • Peng Liu

    (Department of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China)

  • Long Li

    (Department of Computer Science and Technology, Jilin University, Changchun 130012, China)

Abstract

The combination of mobile edge computing (MEC) and wireless power transfer (WPT) is recognized as a promising technology to solve the problem of limited battery capacities and insufficient computation capabilities of mobile devices. This technology can transfer energy to users by radio frequency (RF) in wireless powered mobile edge computing. The user converts the harvested energy, stores it in the battery, and utilizes the harvested energy to execute corresponding local computing and offloading tasks. This paper adopts the Frequency Division Multiple Access (FDMA) technique to achieve task offloading from multiple mobile devices to the MEC server simultaneously. Our objective is to study multiuser dynamic joint optimization of computation and wireless resource allocation under multiple time blocks to solve the problem of maximizing residual energy. To this end, we formalize it as a nonconvex problem that jointly optimizes the number of offloaded bits, energy harvesting time, and transmission bandwidth. We adopt convex optimization technology, combine with Karush–Kuhn–Tucker (KKT) conditions, and finally transform the problem into a univariate constrained convex optimization problem. Furthermore, to solve the problem, we propose a combined method of Bisection method and sequential unconstrained minimization based on Reformulation-Linearization Technique (RLT). Numerical results demonstrate that the performance of our joint optimization method outperforms other benchmark schemes for the residual energy maximization problem. Besides, the algorithm can maximize the residual energy, reduce the computation complexity, and improve computation efficiency.

Suggested Citation

  • Zhiyan Yu & Gaochao Xu & Yang Li & Peng Liu & Long Li, 2021. "Joint Offloading and Energy Harvesting Design in Multiple Time Blocks for FDMA Based Wireless Powered MEC," Future Internet, MDPI, vol. 13(3), pages 1-23, March.
    • Handle: RePEc:gam:jftint:v:13:y:2021:i:3:p:70-:d:516165
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    References listed on IDEAS

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    1. Long Li & Gaochao Xu & Peng Liu & Yang Li & Jiaqi Ge, 2020. "Jointly Optimize the Residual Energy of Multiple Mobile Devices in the MEC–WPT System," Future Internet, MDPI, vol. 12(12), pages 1-18, December.
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    Cited by:

    1. Zhiqiang Dai & Gaochao Xu & Ziqi Liu & Jiaqi Ge & Wei Wang, 2022. "Energy Saving Strategy of UAV in MEC Based on Deep Reinforcement Learning," Future Internet, MDPI, vol. 14(8), pages 1-19, July.

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