Energy Consumption Optimization Algorithm for Full-Duplex Relay-Assisted Mobile Edge Computing Systems
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摘要: 为缓解终端设备处理大数据量、低时延业务的压力，该文提出一种基于全双工中继的移动边缘计算网络资源分配算法。首先，在满足计算任务时延约束、用户最大计算能力、用户和中继的最大发射功率约束条件下，考虑中继选择与子载波分配因子、用户任务卸载系数、用户与中继的传输功率的联合优化，建立了系统总能耗最小化问题。其次，利用交替迭代和变量代换的方法，将原非凸问题分解为两个凸优化子问题，并利用内点法和拉格朗日对偶原理分别进行求解。仿真结果表明，所提算法具有较低的能量消耗。Abstract: In order to alleviate the pressure of terminal devices to deal with the big-data and low-delay services, a resource allocation algorithm is studied for mobile edge computing networks with full-duplex relays. Firstly, the constraints of the maximum task latency, the maximum computing ability of users, and the maximum transmit power of users and relays are considered for achieving total energy consumption minimization by jointly optimizing the relay selection and subcarrier allocation factor, user task offloading coefficient, and the transmission power of users and relays. Secondly, based on the alternating iteration method and the variable-substitution approach, the originally non-convex problem is decomposed into two convex subproblems, which are solved by using the interior-point method and Lagrange dual theory, respectively. Simulation results show that the proposed algorithm has low energy consumption.
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Key words:
- Full-duplex based relays /
- Mobile edge computing /
- Resource allocation
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算法1 基于交替迭代的资源分配算法 1.初始化系统参数：$ p_n^{\max } $,$ P_m^{\max } $, $ T_n^{\max } $, $ F_n^{\max } $, $ \kappa $, $ B $, $K$，$ h_{n,m}^k $, $ {\sigma ^2} $, $ g_m^k $, $f_n^{\text{M}}$, ${S_n}$, ${C_n}$, $\varphi $, $\sigma _{{\text{SI}}}^2$；定义交替迭代算法收敛精度$\ell $；初始化交替迭
代次数$t$；定义外层最大迭代次数${T_{\max }}$；定义Dinkelbach迭代算法收敛精度$\zeta $以及相应最大迭代次数${L_{\max }}$；初始化梯度迭代次数$l$，初始化
$q = 0$；
2.给定$ \alpha _{n,m}^k(t) $和$ \bar p_{n,m}^k(t) $，利用内点法求解问题式(18)，得到$ x_{n,m}^k(t + 1) $；
3.给定$ x_{n,m}^k(t + 1) $，给定q，求解问题式(20)，得到当前最优值$ \bar p_{n,m}^k(l) $，$ \alpha _{n,m}^k(l) $；
4.当$ \left| {x_{n,m}^k(t + 1){S_n}\bar p_{n,m}^k(l) - q\bar R_{n,m}^k(l)} \right| \ge \zeta $，或者$l \le {L_{\max }}$；
5.令Flag=0，更新$l = l + 1$；
6.将$q$更新为$ q = {{x_{n,m}^k(t + 1){S_n}\bar p_{n,m}^k(l)} \mathord{\left/ {\vphantom {{x_{n,m}^k(t + 1){S_n}\bar p_{n,m}^k(l)} {\bar R_{n,m}^k(l)}}} \right. } {\bar R_{n,m}^k(l)}} $，结束并执行步骤3；
7.当$ \left| {x_{n,m}^k(t + 1){S_n}\bar p_{n,m}^k(l) - q\bar R_{n,m}^k(l)} \right| \le \zeta $，或者$l = {L_{\max }}$;
8.令Flag=1，更新并输出$ \alpha _{n,m}^{}(t + 1){\text{ = }}\alpha _{n,m}^l(l) $，$ \bar p_{n,m}^k(t + 1) = \bar p_{n,m}^k(l) $；
9.当$ \left| {\{ E_n^{\text{L}}(t + 1) + \alpha _{n,m}^k(t + 1)E_n^{{\text{UT}}}(t + 1)\} - \{ E_n^{\text{L}}(t) + \alpha _{n,m}^k(t)E_n^{{\text{UT}}}(t)\} } \right| \ge \ell $，或者$t \le {T_{\max }}$；
10.更新$t = t + 1$，执行步骤2；
11.结束并输出。
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表 1 仿真参数
参数 值 参数 值 $\phi $/(${\text{cycles} } \cdot {\text{bi} }{ {\text{t} }^{ {{ - 1} } } }$) 40 $\kappa $ ${10^{ - 24}}$ $F_n^{\max }$/cycles ${10^9}$ $ P_m^{\max } $/W 5 $ f_n^M $/cycles ${10^{10}}$ $ p_n^{\max } $/W 1 $K$ 5 $ \chi $ 3 ${\sigma ^2}$/mW ${10^{ - 6}}$ $ B $/Hz ${10^6}$
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