Energy Consumption Optimization in UAV Wireless Power Transfer Based Mobile Edge Computing System
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摘要: 无线能量传输(WPT)和移动边缘计算(MEC)可以为无线设备提供能量供应和任务计算，有效提高设备的能量效率。该文提出一种基于无人机无线能量传输的边缘计算系统能耗优化方法，在所提方法中，通过联合优化能量收集(EH)时间、用户发射功率和卸载决策，最小化系统总能耗。利用块坐标下降法(BCD)，将优化问题分解为两个子问题，通过交替优化来获得最优能量收集时间、用户发射功率和卸载决策。仿真结果表明，该文提出的系统能耗优化方法优于其他基准方案，并且系统所需能量可以显著减少。Abstract: Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT) can provide energy and task calculation for wireless device, improving effectively the energy efficiency. In this paper, an energy consumption optimization method in UAV wireless power transfer based mobile edge computing system is proposed. In the proposed method, the total energy consumption of the system is minimized through joint optimization of Energy Harvesting (EH) time, user transmit power and offloading strategy. By utilizing Block Coordinate Descent (BCD) method, the optimization problem is divided into two subproblems. The optimal EH time, user transmit power and offloading strategy are obtained through alternate iteration. Simulation results show that the proposed energy consumption optimization method outperforms other benchmark schemes, in which the energy consumption of the system can be significantly reduced.
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表 1 优化过程
输出：联合优化卸载决策$ {\alpha _i} $、能量收集时间τ (1) 初始化卸载策略$ {\alpha ^0}\left( 0 \right) = \left[ {0,0, \cdots ,0} \right] $，u=0 (2) 通过CVX求解(P1.1) 得$ \tau \left( {{\alpha ^u}\left( 0 \right)} \right) $ (3) 求$ {E_{{T_i}}}\left( {{\alpha ^u}\left( 0 \right)} \right) $ (4) for$j = 1,2, \cdots ,L$do (5) A[m]= $ {\alpha ^u}\left( j \right) $, m = m+1 (6) 通过CVX求解(P1.1)得$ \tau \left( {{\alpha ^u}\left( j \right)} \right) $ (7) 求$ {E_{{T_i}}}\left( {{\alpha ^u}\left( j \right)} \right) $ (8) 利用式(24)计算价值函数，并储存在价值函组
$r = \left[ {r_1^u,r_2^u, \cdots ,r_L^u} \right]$中(9) End for (10) if $ {r_{\max }}{\text{ = }}\max (r) > 0 $ (11) $ j = \arg \max (r) $ (12) 更新$ {\alpha ^{u + 1}}\left( 0 \right){\text{ = }}{\alpha ^u}\left( j \right) $ (13) 更新u=u+1 (14) 回到步骤(2) (15) else (16) 得到最优结果$ a* = {a^u}\left( 0 \right) $，$ \tau *{\text{ = }}\tau \left( {{\alpha ^u}\left( 0 \right)} \right) $ (17) End if (18) 通过CVX求解(P1.1) 得$ \tau \left( {{\alpha ^u}\left( 0 \right)} \right) $ (19) 求$ {E_{{T_i}}}\left( {{\alpha ^u}\left( 0 \right)} \right) $ (20) for $ z = 2,3, \cdots ,L $ do (21) 利用式(23)更新${j_1},{j_2}, \cdots ,{j_z}$ (22) if ${\alpha ^u}\left( { {j_1},{j_2}, \cdots ,{j_z} } \right) \in$ A (23) 回到步骤(19) (24) else (25) 进行步骤(5)—步骤(8) (26) end if (27) end for (28) if $ {r_{\max }}{\text{ = }}\max (r) > 0 $ (29) $ \left( {{j_1},{j_2}, \cdots ,{j_z}} \right) = \arg \max (r) $ (30) 更新$ {\alpha ^{u + 1}}\left( 0 \right){\text{ = }}{\alpha ^u}\left( {{j_1},{j_2}, \cdots ,{j_z}} \right) $ (31) 更新u=u+1 (32) 回到步骤(18) (33) else (34) 得到最优结果$ a* = {a^u}\left( 0 \right) $，$ \tau *{\text{ = }}\tau \left( {{\alpha ^u}\left( 0 \right)} \right) $ (35) End if 
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表 2 仿真参数设置
参数 含义 数值（单位） H 无人机悬停高度 100 m T 时隙长度 1 s Ci 用户Ui需要处理的计算任务的数据量 80～120 kB K 处理1bit数据量需要的CPU转数 100 $ {\xi _i} $ 有效电容系数 10–27 $ \eta $ 路径损耗因子 2 $ {\sigma ^2} $ 干扰噪声功率 –110 dBm $ {g_i} $ 单位信道增益 –60 dB $ {\upsilon _i} $ 能量转换效率 0.51 P 无人机发射功率 3 W B 带宽 2 MHz $ f_i^{\max } $ 用户最大CPU频率 1 GHz
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