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. Index Terms-Mobile edge computing, resource allocation, unmanned aerial vehicle communications, trajectory optimizationm wireless power transfer.
In REF , a UAV-enabled wireless powered MEC server was studied to minimize the energy consumption consumed at the UAV.
UAV-Enabled Mobile Edge Computing: Offloading Optimization and Trajectory Design
2018 IEEE International Conference on Communications (ICC)
2018 IEEE International Conference on Communications (ICC)
Computer Science Engineering Mathematics