Research on Energy Consumption Minimization for a Data Compression Based NOMA-MEC System
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摘要: 该文研究基于数据压缩的非正交多址-移动边缘计算(NOMA-MEC)系统中系统能耗最小化问题。考虑到部分压缩与卸载方案和基站端计算能力有限等条件，通过联合优化各用户的任务压缩和卸载比例、发射功率以及任务压缩时间等变量，建立一个系统能耗最小化优化问题。为了求解该问题，首先推导出各用户最佳发射功率的闭式表达式。接着利用连续凸逼近(SCA)方法对原问题的非凸约束进行近似，然后提出一个基于SCA的高效迭代算法来求解原问题，从而得到该系统的最佳资源分配方案。最后借助于计算机仿真对所提出方案的性能优势进行验证，仿真结果表明相比于其他基准方案，该文所提方案能有效降低系统能耗。Abstract: The system energy consumption minimization problem is studied for a data compression based Non-Orthogonal Multiple Access-Mobile Edge Computing (NOMA-MEC) system. Considering the partial compression and offloading schemes and the limited computation capacity at the base station, a system energy consumption minimization optimization problem is formulated by jointly optimizing the users’ data compression and offloading ratios, transmit power, data compression time, etc. In order to solve this problem, closed-form expression of each user’s optimal transmit power is firstly derived. Then the Successive Convex Approximation (SCA) method is used to approximate the non-convex constraints of the formulated problem, and An SCA based efficient iterative algorithm is proposed to solve the formulated problem, obtaining the optimal resource allocation scheme of the system. Finally, the simulation results verify the advantages of the proposed scheme via computer simulations and show that compared with other benchmark schemes, the proposed scheme can effectively reduce the system energy consumption.
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Key words:
- Partial compression /
- Resource allocation /
- Energy consumption minimization /
- NOMA
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1 基于SCA的迭代算法
输入：给定初始值$\left( {\left\{ {\alpha _k^0} \right\},\left\{ {\beta _k^0} \right\},t_{\rm o}^0,t_{\rm c}^0} \right)$；设置迭代次数$n = 1$、最大收敛次数$N$和收敛精度$\varepsilon $；设定循环中止标志${\text{Flag}} = 0$； 输出：最优解$ \left( {\left\{ {\alpha _k^*} \right\},\left\{ {\beta _k^*} \right\},t_{\rm c}^{^*},\left\{ {p_k^*} \right\},t_{\rm o}^*} \right) $；最小系统能耗${E^*}$。 (1) 根据$\left( {\left\{ {\alpha _k^0} \right\},\left\{ {\beta _k^0} \right\},t_{\rm o}^0,t_{\rm c}^0} \right)$计算得出中间变量$x_k^0$，进而计算出初始系统能耗为${E^0}$； (2) Repeat (3) 根据初始值计算得出$z_k^0$； (4) 在给定$\left( {\left\{ {z_k^0} \right\},t_{\rm o}^0} \right)$的情况下，利用CVX工具求解问题(21)，并得到其最优解，即：$\left( {\left\{ {\alpha _k^n} \right\},\left\{ {x_k^n} \right\},t_{\rm c}{^n},t_{\rm o}^n} \right)$； (5) 根据上述所得到的最优解计算得出此时的最小系统能耗为${E^n}$； (6) if $\left| {{E^n} - {E^{n - 1}}} \right| < \varepsilon $； (7) 此时最优解即为问题(18)的最优解，即：$ {\alpha }_{k}^{*}={\alpha }_{k}^{n}；{\beta }_{k}^{*}={x}_{k}^{n}/{\alpha }_{k}^{n},\forall k\text{ }；{t}_{\rm o}^{*}={t}_{\rm o}^{n}；{t}_{\rm c}^{*}={t}_{\rm c}^{n} $； $ p_k^ * = {\sigma ^2}/{h_k}\left( {\exp \left( {z_k^0\ln 2/B{t_{\rm o}}} \right) - \exp \left( {z_{k - 1}^0\ln 2/B{t_{\rm o}}} \right)} \right) $；系统最小能耗为${E^*} = {E^n}$； (8) 输出问题(17)的最优解和系统最小能耗，即：$\left( {\left\{ {\alpha _k^*} \right\},\left\{ {\beta _k^*} \right\},\left\{ {p_k^*} \right\},t_{\rm o}^*,t_{\rm c}^*} \right)$；${E^*}$；设置${\text{Flag}} = 1$； (9) else (10) 设置$ {\alpha }_{k}^{0}={\alpha }_{k}^{n}；{x}_{k}^{0}={x}_{k}^{n},\forall k\text{ }；{t}_{\rm o}^{0}={t}_{\rm o}^{n} $；$n = n + 1$； (11) end (12) until ${\text{Flag}} = 1$or$n = N$； 
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