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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[1] 许方敏, 伍丽娇, 王翔, 等. 5G上行链路中基于预测的紧急资源分配方法研究[J]. 电子与信息学报, 2022, 44(2): 611–619.XU Fangmin, WU Lijiao, WANG Xiang, et al. Research on prediction based emergency resource allocation in 5G uplink[J]. Journal of Electronics & Information Technology, 2022, 2022, 44(2): 611–619. [2] ZHOU Fuhui and HU R Q. Computation efficiency maximization in wireless-powered mobile edge computing networks[J]. IEEE Transactions on Wireless Communications, 2020, 19(5): 3170–3184. doi: 10.1109/TWC.2020.2970920 [3] WANG Feng, XU Jie, WANG Xin, et al. Joint offloading and computing optimization in wireless powered mobile-edge computing systems[J]. IEEE Transactions on Wireless Communications, 2018, 17(3): 1784–1797. doi: 10.1109/TWC.2017.2785305 [4] 施丽琴, 叶迎晖, 卢光跃. 无线供能边缘计算网络中系统计算能效最大化资源分配方案[J]. 通信学报, 2020, 41(10): 59–69. doi: 10.11959/j.issn.1000-436x.2020182SHI Liqin, YE Yinghui, and LU Guangyue. Computation energy efficiency maximization based resource allocation scheme in wireless powered mobile edge computing network[J]. Journal on Communications, 2020, 41(10): 59–69. doi: 10.11959/j.issn.1000-436x.2020182 [5] 张海波, 刘香渝, 荆昆仑, 等. 车联网中基于NOMA-MEC的卸载策略研究[J]. 电子与信息学报, 2021, 43(4): 1072–1079. doi: 10.11999/JEIT200017ZHANG Haibo, LIU Xiangyu, JING Kunlun, et al. Research on NOMA-MEC-based offloading strategy in internet of vehicles[J]. Journal of Electronics &Information Technology, 2021, 43(4): 1072–1079. doi: 10.11999/JEIT200017 [6] 黄永明, 郑冲, 张征明, 等. 大规模无线通信网络移动边缘计算和缓存研究[J]. 通信学报, 2021, 42(4): 44–61. doi: 10.11959/j.issn.1000-436x.2021096HUANG Yongming, ZHENG Chong, ZHANG Zhengming, et al. Research on mobile edge computing and caching in massive wireless communication network[J]. Journal on Communications, 2021, 42(4): 44–61. doi: 10.11959/j.issn.1000-436x.2021096 [7] BI Suzhi and ZHANG Yingjun. Computation rate maximization for wireless powered mobile-edge computing with binary computation offloading[J]. IEEE Transactions on Wireless Communications, 2018, 17(6): 4177–4190. doi: 10.1109/TWC.2018.2821664 [8] ZHAO Nan, LU Weidang, SHENG Min, et al. UAV-assisted emergency networks in disasters[J]. IEEE Wireless Communications, 2019, 26(1): 45–51. doi: 10.1109/MWC.2018.1800160 [9] LU Weidang, DING Yu, GAO Yuan, et al. Resource and trajectory optimization for secure communications in dual unmanned aerial vehicle mobile edge computing systems[J]. IEEE Transactions on Industrial Informatics, 2022, 18(4): 2704–2713. doi: 10.1109/TII.2021.3087726 [10] YE Qibin, LU Weidang, HU Su, et al. Resource optimization in wireless powered cooperative mobile edge computing systems[J]. Science China Information Sciences, 2021, 64(8): 182303. doi: 10.1007/s11432-020-2925-1 [11] HAN Dongsheng and SHI Tianhao. Secrecy capacity maximization for a UAV-assisted MEC system[J]. China Communications, 2020, 17(10): 64–81. doi: 10.23919/JCC.2020.10.005 [12] ZHANG Liang and ANSARI N. Latency-aware IoT service provisioning in UAV-aided mobile-edge computing networks[J]. IEEE Internet of Things Journal, 2020, 7(10): 10573–10580. doi: 10.1109/JIOT.2020.3005117 [13] HU Zhenzhen, ZENG Fanzi, XIAO Zhu, et al. Computation efficiency maximization and QoE-provisioning in UAV-enabled MEC communication systems[J]. IEEE Transactions on Network Science and Engineering, 2021, 8(2): 1630–1645. doi: 10.1109/TNSE.2021.3068123 [14] 张广驰, 曾志超, 崔苗, 等. 无线供电混合多址接入网络的最优能效资源分配研究[J]. 电子学报, 2020, 48(4): 697–705. doi: 10.3969/j.issn.0372-2112.2020.04.011ZHANG Guangchi, ZENG Zhichao, CUI Miao, et al. Optimal energy efficient resource allocation for wireless powered hybrid multiple access networks[J]. Acta Electronica Sinica, 2020, 48(4): 697–705. doi: 10.3969/j.issn.0372-2112.2020.04.011 [15] DU Yao, YANG Kun, WANG Kezhi, et al. Joint resources and workflow scheduling in UAV-enabled wirelessly-powered MEC for IoT systems[J]. IEEE Transactions on Vehicular Technology, 2019, 68(10): 10187–10200. doi: 10.1109/TVT.2019.2935877 [16] LU Weidang, SI Peiyuan, LU Fangwei, et al. Resource and trajectory optimization in UAV-powered wireless communication system[J]. Science China Information Sciences, 2021, 64(4): 140304. doi: 10.1007/s11432-020-3060-4 [17] XIE Lifeng, XU Jie, and ZHANG Rui. Throughput maximization for UAV-enabled wireless powered communication networks[J]. IEEE Internet of Things Journal, 2019, 6(2): 1690–1703. doi: 10.1109/JIOT.2018.2875446 [18] LIU Yuan, XIONG Ke, NI Qiang, et al. UAV-assisted wireless powered cooperative mobile edge computing: Joint offloading, CPU control, and trajectory optimization[J]. IEEE Internet of Things Journal, 2020, 7(4): 2777–2790. doi: 10.1109/JIOT.2019.2958975 [19] ZHOU Fuhui, WU Yongpeng, HU R Q, et al. Computation rate maximization in UAV-enabled wireless-powered mobile-edge computing systems[J]. IEEE Journal on Selected Areas in Communications, 2018, 36(9): 1927–1941. doi: 10.1109/JSAC.2018.2864426 [20] HUANG Liang, BI Suzhi, and ZHANG Y J A. Deep reinforcement learning for online computation offloading in wireless powered mobile-edge computing networks[J]. IEEE Transactions on Mobile Computing, 2020, 19(11): 2581–2593. doi: 10.1109/TMC.2019.2928811 [21] 崔高峰, 徐媛媛, 张尚宏, 等. 基于最小能耗的多无人机无线网络安全数据卸载策略[J]. 通信学报, 2021, 42(5): 51–62. doi: 10.11959/j.issn.1000-436x.2021085CUI Gaofeng, XU Yuanyuan, ZHANG Shanghong, et al. Secure data offloading strategy for multi-UAV wireless networks based on minimum energy consumption[J]. Journal on Communications, 2021, 42(5): 51–62. doi: 10.11959/j.issn.1000-436x.2021085 -
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