Wireless Powered Mobile Edge Computing Systems: Simultaneous Time Allocation and Offloading Policies

To improve the computational power and limited battery capacity of mobile devices (MDs), wireless powered mobile edge computing (MEC) systems are gaining much importance. In this paper, we consider a wireless powered MEC system composed of one MD and a hybrid access point (HAP) attached to MEC. Our objective is to achieve a joint time allocation and offloading policy simultaneously. We propose a cost function that considers both the energy consumption and the time delay of an MD. The proposed algorithm, joint time allocation and offload policy (JTAOP), is used to train a neural network for reducing the complexity of our algorithm that depends on the resolution of time and the number of components in a task. The numerical results are compared with three benchmark schemes, namely, total local computation, total offloading and partial offloading. Simulations show that the proposed algorithm performs better in producing the minimum cost and energy consumption as compared to the considered benchmark schemes.

Automated summary

This paper investigates a joint time allocation and offloading policy in wireless powered mobile edge computing systems. A cost function considering energy consumption and time delay is proposed, and a JTAOP algorithm is designed to train a neural network to reduce algorithm complexity. Numerical results show that the algorithm outperforms other benchmark schemes in minimizing cost and energy consumption.