Dynamic Radio Resource and Task Allocation for Wireless Powered Mobile Edge Computing System

Limited capacities in computation and battery of Internet of things (IoT) devices are two main bottlenecks for quality of service. Emerging mobile edge computing (MEC) and radio frequency based wireless power transfer (WPT) can help alleviate the issues. Incorporating WPT into MEC, IoT devices can get sustainable energy supply by WPT, and offload computation tasks to MEC to improve the computing ability. In this paper, we jointly consider the radio resource and task allocation for the wireless powered MEC system. To capture the high dynamics in task arrival and wireless network, a stochastic optimization problem which minimizes the energy consumption while guaranteeing queue stability is formulated. By exploiting the stochastic optimization theory, we transform the original problem into a deterministic optimization problem. A radio resource and task allocation (RRTA) algorithm is designed to acquire the optimal solutions of this problem. Theoretical analysis shows that RRTA can achieve arbitrary tradeoff between the energy consumption and queue length. Moreover, the close-to-optimal energy consumption can be reached by RRTA while bounding the queueing length. Experiment results reveal that RRTA can effectively decrease the energy consumption and maintain a small queue length.