Execution Delay Minimization in Wireless Powered Mobile Edge Computing Networks
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摘要: 针对无线供能移动边缘计算(MEC)网络，该文将计算时延定义为数据卸载与计算所消耗的时间，并提出一种节点计算时延之和最小化的多维资源分配方法。首先，在节点能量因果约束下，通过联合优化专用能量站工作时长、任务分割系数、节点计算频率和发射功率来建立一个计算时延之和最小化的多维资源分配问题。由于存在优化变量耦合与max-max函数，所建问题非凸且无法采用凸优化工具获取最优解。为此，通过引入一系列松弛变量和辅助变量来进行优化问题简化以及优化变量解耦，并在此基础上，通过深入分析简化问题的结构特性，提出一种基于二分法的迭代算法来求解原问题的最优解。最后，计算机仿真验证了所提迭代算法的正确性以及所提资源分配方法在计算时延方面的优越性。
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关键词:
- 无线供能移动边缘网络 /
- 计算时延 /
- 能量因果约束 /
- 资源分配方法
Abstract: For a wireless powered MEC (Mobile Edge Computing) network, the execution delay as the time for data offloading and data execution is defined, and a multidimensional resource allocation scheme is proposed to minimize the execution delay of all nodes. Firstly, an execution delay minimization based multidimensional optimization problem is formulated by jointly optimizing the operation time of a power beacon, the portions of task bits for local computing and offloading, the computing frequency and the transmit power of per node, subject to the energy-causality constraint of nodes. As the formulated optimization problem includes couplings among optimization variables and the max-max function, it is non-convex and can not be solved by the existing convex tools. Therefore, a series of slack variables and auxiliary variables are introduced to simplify the optimization problem and decouple the coupled variables. Then after carefully inspecting the structure of the simplified problem, a dichotomy based iterative algorithm is proposed to obtain the optimal solution. Finally, computer simulations validate the correctness of the devised iterative algorithm and the advantages of the proposed resource allocation in terms of the execution delay. -
表 1 仿真参数
含义 参数 数值 用户数 K 4 专用能量站最大发射功率 P0 23 dBm 带宽 W 400 kHz 噪声功率谱密度 $ {{{\sigma ^{\text{2}}}} \mathord{\left/ {\vphantom {{{\sigma ^{\text{2}}}} W}} \right. } W} $ –120 dBm/Hz 节点k最小所需计算的任务比特数 Lk 5 kbit 节点k发送信息的电路损耗 $ {p_{k,c}} $ 1 mW 节点k本地计算时最大计算频率 $ f_k^{\max } $ 500 MHz 节点k本地计算时有效电容系数 $ {\varepsilon _k} $ 10–26 节点k计算一个比特所需要的CPU 时钟周期数 Gk 1000 Cycles/bit 
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