SAG-MEC网络下支持WPT的无人机动态任务卸载与资源分配

doi:10.19678/j.issn.1000-3428.0070030

摘要/Abstract

摘要：

针对偏远地区蜂窝网络覆盖不足且物联网(IoT)设备能量和计算能力低而无法满足大量延迟敏感型任务卸载和计算需求的问题, 考虑将空天地一体化网络(SAGIN)和移动边缘计算(MEC)相结合, 提出一种支持无线电力传输(WPT)技术的无人机辅助IoT设备的动态任务卸载和资源分配方案, 其中无人机负责收集IoT设备产生的计算密集型任务, 采用部分卸载模式将这些任务根据当前状态进行本地计算或动态卸载给基站和低地球轨道(LEO)卫星进一步处理。由于动态的异构网络和长期排队延迟与短期决策的耦合性, 因此在排队延迟的约束下提出一种基于Lyapunov优化的双延迟深度确定性策略梯度(TD3PG)算法, 该算法通过优化无人机动态关联、任务分配、计算资源分配和带宽分配来协调无人机学习最优卸载策略和资源分配。仿真结果表明, 所提出的动态方案与其他对比方案相比能够有效降低无人机网络的能耗、网络积压总和及平均排队延迟, 在2种学习率组合下TD3PG算法相对于深度确定性策略梯度(DDPG)算法和双重深度Q网络(DDQN)算法的奖励分别提高了13.6%、24.0%和20.4%、17.9%。

关键词: 空天地一体化网络, 移动边缘计算, 无人机, 任务卸载, 资源分配

Abstract:

Remote areas face problems such as insufficient cellular network coverage as well as low energy and computing power of Internet of Things (IoT) devices. Hence, the requirements for delay-sensitive task offloading and computing for a large number of tasks cannot be met. Considering the combination of the Space—Air—Ground Integrated Network (SAGIN) and Mobile Edge Computing (MEC), this paper proposes a strategy for dynamic task offloading and resource allocation for Unmanned Aerial Vehicle (UAV)-assisted IoT devices that support Wireless Power Transmission (WPT) technology, in which UAVs are responsible for collecting compute-intensive tasks generated by IoT devices. These tasks are locally calculated or dynamically unloaded to the base station and a Low Earth Orbit (LEO) satellite for further processing using a partial unloading mode, according to the current state. Given the dynamic heterogeneous network environment, as well as the tight coupling between long-term queuing delays and short-term decision-making, this paper proposes a Twin Delayed Deep Deterministic Policy Gradient (TD3PG) algorithm based on Lyapunov optimization under queuing delay constraints. The algorithm coordinates UAVs to learn the optimal offloading strategy and resource allocation by optimizing UAV dynamic association, task allocation, computing resource allocation, and bandwidth allocation. Simulation results show that, compared with other schemes, the proposed dynamic scheme can effectively reduce the energy consumption, network backlog sum, and average queue delay in the UAV network. Under different learning rate combinations, the reward of the TD3PG algorithm increases by 13.6% and 24.0% compared with that of the Deep Deterministic Policy Gradient (DDPG) algorithm, and by 20.4% and 17.9% compared with that of the Double Deep Q-Network (DDQN) algorithm.

Key words: Space—Air—Ground Integrated Network (SAGIN), Mobile Edge Computing (MEC), Unmanned Aerial Vehicle (UAV), task offloading, resource allocation

王怡, 覃团发, 韦睿, 黄金宝. SAG-MEC网络下支持WPT的无人机动态任务卸载与资源分配[J]. 计算机工程, 2026, 52(5): 371-382.

WANG Yi, QIN Tuanfa, WEI Rui, HUANG Jinbao. Dynamic Task Offloading and Resource Allocation of UAVs Supported by WPT in SAG-MEC Network[J]. Computer Engineering, 2026, 52(5): 371-382.

https://www.ecice06.com/CN/Y2026/V52/I5/371

图/表 9

图1 面向SAG-MEC网络的任务卸载与计算模型

Fig.1 Task offloading and computing model for SAG-MEC network

图2 TD3PG框架结构

Fig.2 TD3PG framework structure

图3 不同Episode的网络累积奖励

Fig.3 Cumulative network rewards of different Episodes

图4 不同时隙的网络延迟积压总和

Fig.4 Total network delay backlogs of different time slots

图5 无人机最大计算能力下无人机网络能量消耗

Fig.5 UAV network energy consumption under UAV maximum computing ability

图6 到达任务量对无人机网络能耗的影响

Fig.6 Effect of task arrivals on UAV network energy consumption

图7 不同时隙下本地计算的平均排队延迟

Fig.7 Average queue delay of local computing at different time slots

参考文献 30

1	BEDI G , VENAYAGAMOORTHY G K , SINGH R , et al. Review of Internet of Things (IoT) in electric power and energy systems. IEEE Internet of Things Journal, 2018, 5 (2): 847- 870.
2	WANG H C , WANG J L , DING G R , et al. Robust spectrum sharing in air-ground integrated networks: opportunities and challenges. IEEE Wireless Communications, 2020, 27 (3): 148- 155. doi: 10.1109/MWC.001.1900398
3	SAHOO B P S , PUTHAL D , SHARMA P K . Toward advanced UAV communications: properties, research challenges, and future potential. IEEE Internet of Things Magazine, 2022, 5 (1): 154- 159. doi: 10.1109/IOTM.002.2100085
4	LI Z D , WANG Y , LIU M , et al. Energy efficient resource allocation for UAV-assisted space — air — ground Internet of remote things networks. IEEE Access, 2019, 7, 145348- 145362. doi: 10.1109/ACCESS.2019.2945478
5	AI B , MOLISCH A F , RUPP M , et al. 5G key technologies for smart railways. Proceedings of the IEEE, 2020, 108 (6): 856- 893. doi: 10.1109/JPROC.2020.2988595
6	JIA Z Y , SHENG M , LI J D , et al. LEO-satellite-assisted UAV: joint trajectory and data collection for Internet of remote things in 6G aerial access networks. IEEE Internet of Things Journal, 2020, 8 (12): 9814- 9826.
7	刘亮, 毛武平, 李汶蔚, 等. 空天地一体化边缘计算网络中基于博弈论的任务卸载策略. 计算机工程, 2025, 51 (2): 238- 249. doi: 10.19678/j.issn.1000-3428.0069161
	LIU L , MAO W P , LI W W , et al. Task offloading strategy based on game theory in the space — air — ground integrated edge computing networks. Computer Engineering, 2025, 51 (2): 238- 249. doi: 10.19678/j.issn.1000-3428.0069161
8	TANG Q Q , LI B . Overview of mobile edge computing in space — air — ground integrated network. Radio Communications Technology, 2021, 47 (1): 25- 35.
9	CUI H X , ZHANG J , GENG Y H , et al. Space — Air — Ground Integrated Network (SAGIN) for 6G: requirements, architecture and challenges. China Communications, 2022, 19 (2): 90- 108. doi: 10.23919/JCC.2022.02.008
10	JI B F , WANG Y N , SONG K , et al. A survey of computational intelligence for 6G: key technologies, applications and trends. IEEE Transactions on Industrial Informatics, 2021, 17 (10): 7145- 7154. doi: 10.1109/TII.2021.3052531
11	LIU J J , SHI Y P , FADLULLAH Z M , et al. Space — air — ground integrated network: a survey. IEEE Communications Surveys & Tutorials, 2018, 20 (4): 2714- 2741.
12	MAO B M , TANG F X , KAWAMOTO Y , et al. Optimizing computation offloading in satellite-UAV-served 6G IoT: a deep learning approach. IEEE Network, 2021, 35 (4): 102- 108. doi: 10.1109/MNET.011.2100097
13	李晓青, 贺占权, 周卫彤. 支持MEC的天地一体化网络下任务卸载和资源分配. 计算机应用与软件, 2023, 40 (2): 130- 137.
	LI X Q , HE Z Q , ZHOU W T . Task offloading and resource allocation for the MEC enabled integrated satellite-terrestrial network. Computer Applications and Software, 2023, 40 (2): 130- 137.
14	LIU J Y , ZHAO X W , QIN P , et al. Joint dynamic task offloading and resource scheduling for WPT enabled space — air — ground power Internet of Things. IEEE Transactions on Network Science and Engineering, 2021, 9 (2): 660- 677.
15	TANG Q Q , FEI Z S , LI B , et al. Computation offloading in LEO satellite networks with hybrid cloud and edge computing. IEEE Internet of Things Journal, 2021, 8 (11): 9164- 9176. doi: 10.1109/JIOT.2021.3056569
16	ZHANG S B , LIU A J , HAN C , et al. Multiagent reinforcement learning-based orbital edge offloading in SAGIN supporting Internet of remote things. IEEE Internet of Things Journal, 2023, 10 (23): 20472- 20483. doi: 10.1109/JIOT.2023.3287737
17	CHEN Y L , AI B , NIU Y , et al. Energy-constrained computation offloading in space — air — ground integrated networks using distributionally robust optimization. IEEE Transactions on Vehicular Technology, 2021, 70 (11): 12113- 12125. doi: 10.1109/TVT.2021.3116593
18	ZHOU C H , WU W , HE H L , et al. Deep reinforcement learning for delay-oriented IoT task scheduling in SAGIN. IEEE Transactions on Wireless Communications, 2021, 20 (2): 911- 925. doi: 10.1109/TWC.2020.3029143
19	ZHOU C H, WU W, HE H L, et al. Delay-aware IoT task scheduling in space — air — ground integrated network[C]//Proceedings of the IEEE Global Communications Conference. Washington D.C., USA: IEEE Press, 2019: 1-9.
20	李斌, 刘文帅, 费泽松. 面向空天地异构网络的边缘计算部分任务卸载策略. 电子与信息学报, 2022, 44 (9): 3091- 3098.
	LI B , LIU W S , FEI Z S . Partial computation offloading for mobile edge computing in space — air — ground integrated network. Journal of Electronics & Information Technology, 2022, 44 (9): 3091- 3098.
21	LIU Y D, LI B, YANG Y X, et al. Joint delay and completion rate optimization for dependent task offloading in space — air — ground integrated network[C]//Proceedings of the 9th International Conference on Computer and Communications (ICCC). Washington D.C., USA: IEEE Press, 2023: 327-331.
22	ZHENG K C , JIANG G D , LIU X Y , et al. DRL-based offloading for computation delay minimization in wireless-powered multi-access edge computing. IEEE Transactions on Communications, 2023, 71 (3): 1755- 1770. doi: 10.1109/TCOMM.2023.3237854
23	WANG Y H , LI B , HE J H , et al. Joint latency-oriented, energy consumption, and carbon emission for a space — air — ground integrated network with newly designed power technology. Electronics, 2023, 12 (17): 3537. doi: 10.3390/electronics12173537
24	QIN P , FU Y , XIE Y B , et al. Multi-agent learning-based optimal task offloading and UAV trajectory planning for AGIN-power IoT. IEEE Transactions on Communications, 2023, 71 (7): 4005- 4017. doi: 10.1109/TCOMM.2023.3274165
25	LITTLE J D C . A proof for the queuing formula: L=λW. Operations Research, 1961, 9 (3): 383- 387. doi: 10.1287/opre.9.3.383
26	ZHANG Z B , LI X H , AN J P , et al. Model-free attitude control of spacecraft based on PID-guide TD3 algorithm. International Journal of Aerospace Engineering, 2020 (1): 8874619.
27	KUMAR A S , ZHAO L , FERNANDO X . Task offloading and resource allocation in vehicular networks: a Lyapunov-based deep reinforcement learning approach. IEEE Transactions on Vehicular Technology, 2023, 72 (10): 13360- 13373. doi: 10.1109/TVT.2023.3271613
28	MA G F , WANG X W , HU M J , et al. DRL-based computation offloading with queue stability for vehicular-cloud-assisted mobile edge computing systems. IEEE Transactions on Intelligent Vehicles, 2023, 8 (4): 2797- 2809. doi: 10.1109/TIV.2022.3225147
29	WU Q Q , ZENG Y , ZHANG R . Joint trajectory and communication design for multi-UAV enabled wireless networks. IEEE Transactions on Wireless Communications, 2018, 17 (3): 2109- 2121. doi: 10.1109/TWC.2017.2789293
30	DENG D , LI J X , JHAVERI R H , et al. Reinforcement-learning-based optimization on energy efficiency in UAV networks for IoT. IEEE Internet of Things Journal, 2023, 10 (3): 2767- 2775. doi: 10.1109/JIOT.2022.3214860

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