Energy Efficiency and Delay Tradeoff in Multi-User Wireless Powered Mobile-Edge Computing Systems

Prolonging battery lifetime and enhancing computation capability have been the key challenges for designing the mobile devices in the Internet of Things (IoT) era. The investigation of Mobile-Edge Computing (MEC) with Wireless Energy Transfer (WET) is a promising solution to overcome such challenges. In this paper, we study the fundamental tradeoff between Energy Efficiency (EE) and delay in the multi-user wireless powered MEC systems. In order to tackle the randomness of channel conditions and task arrivals, we formulate a stochastic optimization problem to achieve the EE-delay tradeoff, which optimizes the network energy efficiency subject to the network stability, Central Processing Unit (CPU)-cycle frequency, peak transmission power, and energy causality constraints. Furthermore, we propose a joint computation allocation and resource management algorithm by transforming the original problem into a series of deterministic optimization problems in each time block based on Lyapunov optimization theory, whose convexity is further proved. Specifically, the proposed algorithm with low complexity requires no prior distribution knowledge of channel conditions and task arrivals. In addition, theoretical analysis shows that the algorithm achieves the EE-delay tradeoff as O(1/V ),O(V ) and provides a control parameter V to balance the EE-delay performance. Numerical results verify the theoretical analysis and reveal the impacts of various parameters to the system performance.