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.

中文翻译：

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具有多接入方案的无线供电移动边缘计算系统的能源效率和延迟权衡

移动边缘计算 (MEC) 和无线能量传输 (WET) 的集成已被公认为是在物联网 (IoT) 时代增强计算能力和延长资源受限无线设备电池寿命的有前途的技术。然而，联合调度能量、无线电和计算资源以协调无线供电 MEC 系统中的异构性能要求具有挑战性。为了填补这一空白，本文研究了多用户无线供电 MEC 系统中能源效率 (EE) 和延迟之间的基本权衡。考虑到随机信道条件和任务到达，我们制定了一个随机优化问题来研究 EE-延迟权衡，在网络稳定性、最大中央处理器频率、峰值传输功率、可用通信资源和能源因果关系约束。进一步，我们基于李雅普诺夫优化理论，通过将原始问题转化为每个时间块中的一系列确定性优化问题，提出了在线计算卸载和资源分配算法。此外，理论分析表明该算法实现了EE-delay权衡[ ${O}(1/{V}), {O}({V})$ ]，并引入了控制参数${V}$平衡EE延迟性能。数值结果验证了理论分析，揭示了各种参数对系统性能的影响。基于李雅普诺夫优化理论，我们通过将原问题转化为每个时间块中的一系列确定性优化问题，提出了在线计算卸载和资源分配算法。此外，理论分析表明该算法实现了EE-delay权衡[ ${O}(1/{V}), {O}({V})$ ]，并引入了控制参数${V}$平衡EE延迟性能。数值结果验证了理论分析，揭示了各种参数对系统性能的影响。基于李雅普诺夫优化理论，我们通过将原问题转化为每个时间块中的一系列确定性优化问题，提出了在线计算卸载和资源分配算法。此外，理论分析表明该算法实现了EE-delay权衡[ ${O}(1/{V}), {O}({V})$ ]，并引入了控制参数${V}$平衡EE延迟性能。数值结果验证了理论分析，揭示了各种参数对系统性能的影响。