Optimization of Wireless Power Transfer for Wireless-Powered Mobile Edge Computing

Wireless-powered mobile edge computing (WP-MEC) is a new network computing paradigm, which integrates with the advantages of wireless power transfer and mobile edge computing. In WP-MEC, the time of wireless power transfer is a key factor affecting the performance of network system when the harvest-then-offload protocol is employed. If the time of wireless power transfer is too short, the user cannot harvest enough energy; If it is too long, the task uploading time left for the user is insufficient. Both result in numerous user tasks being discarded. To tackle this problem, an optimization algorithm for the time of wireless power transfer based on differential evolution is proposed. The hybrid mutation operator and perturbation based binomial crossover operator are designed for this algorithm. These two improvements effectively enhance the optimization performance of DE, conducive to find the optimal time of wireless power transfer. Moreover, the micro-population is introduced into this algorithm in order to improve the optimization efficiency. Finally, the performance of this algorithm is verified through the existing computation completion ratio maximization model under the WP-MEC scenario with multiple edge servers. Numerical results show that the computation offloading scheme incorporating this algorithm can achieve a higher computation completion rate of user tasks than the benchmark schemes. This proves that the proposed algorithm can find a better wireless power transfer time than the benchmark schemes and is an effective wireless power transfer time optimization scheme.