Mobile Edge Computing (MEC) enables rich services in close proximity to the end users to provide high quality of experience (QoE) and contributes to energy conservation compared with local computing, but results in increased communication latency. In this paper, we investigate how to jointly optimize task offloading and resource allocation to minimize the energy consumption in an orthogonal frequency division multiple access-based MEC networks, where the time-sensitive tasks can be processed at both local users and MEC server via partial offloading. Since the optimization variables of the problem are strongly coupled, we first decompose the orignal problem into three subproblems named as offloading selection (PO ), transmission power optimization (PT ), and subcarriers and computing resource allocation (PS ), and then propose an iterative algorithm to deal with them in a sequence. To be specific, we derive the closed-form solution for PO , employ the equivalent parametric convex programming to cope with the objective function which is in the form of sum of ratios in PT , and deal with PS by an alternating way in the dual domain due to its NP-hardness. Simulation results demonstrate that the proposed algorithm outperforms the existing schemes.

中文翻译：

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具有移动边缘计算的时间敏感网络中的能量感知卸载

与本地计算相比，移动边缘计算 (MEC) 使靠近最终用户的丰富服务能够提供高质量的体验 (QoE) 并有助于节能，但会导致通信延迟增加。在本文中，我们研究了如何在基于正交频分多址的 MEC 网络中联合优化任务卸载和资源分配以最小化能量消耗，其中时间敏感的任务可以在本地用户和 MEC 服务器上通过部分卸载。由于问题的优化变量是强耦合的，我们首先将原始问题分解为三个子问题，分别称为卸载选择（PO）、传输功率优化（PT）以及子载波和计算资源分配（PS），然后提出一个迭代算法来依次处理它们。具体来说，我们推导出PO 的闭式解，采用等效参数凸规划来处理PT 中的比和形式的目标函数，并在对偶域中以交替的方式处理PS由于其 NP 硬度。仿真结果表明，所提出的算法优于现有方案。