Energy Efficiency and Delay Tradeoff for Wireless Powered Mobile-Edge Computing Systems With Multi-Access Schemes

• Published in: IEEE transactions on wireless communications Vol. 19; no. 3; pp. 1855 - 1867
• Main Authors: Mao, Sun, Leng, Supeng, Maharjan, Sabita, Zhang, Yan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Central processing units; Computation offloading; Constraints; CPUs; Delay; Delays; Edge computing; Energy efficiency; Internet of Things; Mobile computing; Mobile-edge computing; Optimization; Parameters; Performance evaluation; Power efficiency; Resource allocation; Resource management; Schedules; Service life assessment; Task analysis; Tradeoffs; Wireless communication; wireless energy transfer; Wireless power transmission
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2019.2959300

The integration of Mobile-edge Computing (MEC) and Wireless Energy Transfer (WET) has been recognized as a promising technique to enhance computation capability and to prolong battery lifetime of resource-constrained wireless devices in the Internet of Things (IoT) era. However, it is challenging to jointly schedule energy, radio, and computational resources for coordinating heterogeneous performance requirements in wireless powered MEC systems. To fill this gap, this paper investigates the fundamental tradeoff between Energy Efficiency (EE) and delay in a multi-user wireless powered MEC system. Considering the random channel conditions and task arrivals, we formulate a stochastic optimization problem to study the EE-delay tradeoff, which optimizes network EE subject to network stability, maximum central processing unit frequency, peak transmission power, available communication resource, and energy causality constraints. Further, we propose the online computation offloading and resource allocation algorithm by transforming the original problem into a series of deterministic optimization problems in each time block based on Lyapunov optimization theory. In addition, theoretical analysis shows that the algorithm achieves the EE-delay tradeoff as [O(1/V), O(V)] and introduces a control parameter V to balance the EE-delay performance. Numerical results verify the theoretical analysis and reveal the impact of various parameters to the system performance.