Fair Energy Efficiency Scheduling in NOMA-Based Mobile Edge Computing
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摘要: 将移动边缘计算技术(MEC)与非正交多址技术(NOMA)结合，同时考虑公平性，该文研究了采用NOMA上行部分卸载的MEC系统公平能效问题。首先将基于公平函数的用户速率与功耗比值定义为公平能效函数，随后提出了两种公平能效调度准则下的能效调度算法，即最大化最小速率准则下DK-SCA算法及最大化系统能效准则下DK-SCALE算法，通过算法实现分别得到两种公平能效调度准则下用户最佳本地CPU处理频率及最佳传输功率。最后通过仿真表明，与基准方案相比，所提基于NOMA的部分卸载方案能够有效地将本地计算和基于NOMA的边缘卸载结合，达到最佳的公平能效性能。Abstract: Combing Mobile Edge Computing (MEC) and Non-Orthogonal Multiple Access (NOMA) technologies while considering fairness, this paper studies the fair energy efficiency of the MEC system using NOMA partial offloading. First, the ratio of user rate to power consumption based on the fair function is defined as the fair energy efficiency function. Then, two energy efficiency scheduling algorithms under the fair energy efficiency scheduling criteria are proposed, namely the DK-SCA algorithm under the maximum-minimum rate criterion and the DK-SCALE algorithm under the maximum system energy efficiency criterion. The optimal CPU-frequency cycle and optimal transmit power under these two fair energy efficiency scheduling criteria are obtained, respectively. Finally, simulations show that compared with the benchmark schemes, the proposed NOMA -based partial offloading scheme can effectively combine local computing with edge offloading based on NOMA, which can achieve the best fair energy efficiency performance.
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Key words:
- Edge computing /
- Computing offloading /
- Non-Orthogonal Multiple Access (NOMA) /
- Energy efficiency /
- Fair
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表 1 DK-SCA迭代算法
步骤1：初始化本地计算速度$ f_n^{(0)} $和$ x_n^{{\text{u}}(0)} $，$ {Z^0} $，$ \eta _\infty ^0{\text{ = }}0 $，设置
停止阈值e，迭代次数I；步骤2： for i=1: I； 利用SCA迭代求解${{\text{P}}_{1.4}}$，得到结果
$ \left\{ {f_n^i,x_n^{{\text{u}},i},\eta _\infty ^i{\text{,z}}_n^{u,i}{\text{(k)}}} \right\} $，更新能效暂态值$\eta _\infty ^i{\text{ = } }\dfrac{ { {Z^i} } }{ {\displaystyle\sum\nolimits_{n \in N} {\left( {\zeta {\text{exp} }\left( { {{x} }_n^{u,i} } \right) + {P_r} + \varepsilon f_n^{i3} } \right)} } }\qquad\qquad (19)$ 步骤3：if$||\eta _\infty ^i - \eta _\infty ^{ {\text{i - 1} } }|| \le e$ 获得最佳能效$ \eta _\infty ^*{\text{ = }}\eta _\infty ^i\; $； break； 步骤4：输出最佳能效$ \eta _\infty ^{\text{*}} $。 
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表 2 DK-SCALE迭代算法
步骤1：取$\zeta > 0，{R}_{n}^{\mathrm{min} } > 0，{P}_{n}^{\text{th} }> 0，{\eta }_{0}^{i}$；初始化${{\text{P}}_{2.3}}$，
$ Z(0) $，$ {f_n}(0) $，$ p_n^{\text{u}}(0) $，$ {a_n}(0) $，$ {b_n}(0) $迭代次数I；步骤2：for i =1: I 利用SCALE方法交替迭代求解${{\text{P}}_{2.3}}$，得到近似能效： $\eta _0^i = \frac{{\displaystyle\sum\nolimits_{n \in N} {\left( {W{Z_n}(x_n^u,{a_n},{b_n}) + \frac{{{f_n}}}{{{\gamma _n}}}} \right)} }}{{\displaystyle\sum\nolimits_{n \in N} {\left( {\zeta \exp \left( {x_n^u} \right) + {p_r} + \varepsilon f_n^3} \right)} }} \qquad (29)$ 步骤3：if$||\eta _0^i - \eta _0^{i - 1}|| \le e$ 获得最佳能效$ \eta _0^*{\text{ = }}\eta _0^i\; $； break； 步骤4：输出$( {}{f}_{n}^{*}，{p}_{n}^{\text{u}*}\text{} )$和最佳能效$ \eta _0^{\text{*}} $。
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