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May 1, 2018203 citations

UAV-Enabled Mobile Edge Computing: Offloading Optimization and Trajectory Design

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FZFuhui ZhouYWYongpeng WuHSHaijian Sun

Key Points

  • To minimize total power consumption in a UAV-enabled wireless powered mobile edge computing system while satisfying computation demands and energy harvesting constraints.
  • Formulated a non-convex power minimization problem integrating task offloading, energy transfer causality, and drone trajectory design.
  • Developed an alternating optimization algorithm utilizing sequential convex optimization to solve the non-convex optimization problem.
  • Evaluated power efficiency and convergence rates through numerical simulations against established benchmark schemes.
  • Demonstrated superior energy minimization performance compared to baseline benchmark schemes across evaluated operational settings.
  • Achieved efficient computational convergence through the proposed alternating sequential convex optimization algorithm.

Abstract

With the emergence of diverse mobile applications (such as augmented reality), the quality of experience of mobile users is greatly limited by their computation capacity and finite battery lifetime. Mobile edge computing (MEC) and wireless power transfer are promising to address this issue. However, these two techniques are susceptible to propagation delay and loss. Motivated by the chance of short-distance line-of-sight achieved by leveraging unmanned aerial vehicle (UAV) communications, an UAV-enabled wireless powered MEC system is studied. A power minimization problem is formulated subject to the constraints on the number of the computation bits and energy harvesting causality. The problem is non-convex and challenging to tackle. An alternative optimization algorithm is proposed based on sequential convex optimization. Simulation results show that our proposed design is superior to other benchmark schemes and the proposed algorithm is efficient in terms of the convergence.

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Cite This Study

Zhou et al. (2018) studied this question.

synapsesocial.com/papers/6a19b60205af093a17f67384https://doi.org/10.1109/icc.2018.8422277
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