Intelligent Time Allocation for Wireless Power Transfer in Wireless-Powered Mobile Edge Computing

Wireless-powered mobile edge computing is a new network computing paradigm that combines with the advantages of wireless power transfer and mobile edge computing. When the harvest-then-offload protocol is adopted in this network, the time of wireless power transfer has a significant impact on system performance. If the time is too short, the user cannot harvest enough energy. If it is too long, the user will not have enough time to complete the task offloading. Both result in many of user tasks being discarded. To address this problem, DEWPT, a differential evolution-based optimization scheme for wireless power transfer time, is proposed in this paper. DEWPT is designed with a hybrid mutation operator and a perturbation-based binomial crossover operator. The hybrid mutation operator combines the benefits of two mutation operators with distinct characteristics, so that DEWPT not only has a strong exploration ability but also can quickly converge. Meanwhile, the perturbation-based binomial crossover operator improves DEWPT’s ability to exploit local space. These two improvements effectively enhance DEWPT’s optimization performance, which is beneficial to find the optimal time for wireless power transfer. Furthermore, to improve the optimization efficiency, micro-population is introduced into DEWPT. Finally, the computation completion ratio maximization model is used to validate the performance of DEWPT in the wireless-powered mobile edge computing network with multiple edge servers. Numerical results show that the computation offloading scheme integrating with DEWPT can achieve a higher computation completion rate than three benchmark schemes, and is competitive in complexity. This demonstrates that DEWPT is an effective time allocation scheme for wireless power transfer.