Joint trajectory optimization and computation offloading strategy for space-air-ground integrated networks

ZHU Haoyang; ZHANG Yuexia

doi:10.11959/j.issn.2096-3750.2025.00475

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Joint trajectory optimization and computation offloading strategy for space-air-ground integrated networks

Topic: Satellite Internet of Things | 更新时间：2025-04-21

- Joint trajectory optimization and computation offloading strategy for space-air-ground integrated networks
- Chinese Journal on Internet of Things Vol. 9, Issue 1, Pages: 50-58(2025)
- 作者机构：
  
  北京信息科技大学信息与通信工程学院，北京 102206
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62301058);Beijing Science and Technology Project(Z191100001419012)
- DOI：10.11959/j.issn.2096-3750.2025.00475
  CLC： TN929.5;TN929.5
- Received：14 January 2025，
  
  Revised：2025-02-10，
  
  Published：30 March 2025
- 稿件说明：
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朱昊阳,张月霞.空天地一体化网络中的联合轨迹优化与计算卸载策略[J].物联网学报,2025,09(01):50-58.

ZHU Haoyang,ZHANG Yuexia.Joint trajectory optimization and computation offloading strategy for space-air-ground integrated networks[J].Chinese Journal on Internet of Things,2025,09(01):50-58.
朱昊阳,张月霞.空天地一体化网络中的联合轨迹优化与计算卸载策略[J].物联网学报,2025,09(01):50-58. DOI： 10.11959/j.issn.2096-3750.2025.00475.

ZHU Haoyang,ZHANG Yuexia.Joint trajectory optimization and computation offloading strategy for space-air-ground integrated networks[J].Chinese Journal on Internet of Things,2025,09(01):50-58. DOI： 10.11959/j.issn.2096-3750.2025.00475.

摘要

空天地一体化网络作为一种新型网络架构，能够有效提高网络覆盖范围和服务质量，是未来6G实现万物互联的关键支撑。针对空天地一体化网络中计算资源分配不合理和无人机利用不充分的问题，提出了一种空天地一体化网络中的联合轨迹优化与计算卸载策略。首先，分析了无人机的盘旋模型和地面终端的关联关系。其次，联合考虑了无人机轨迹、无人机与地面终端的匹配因子、任务卸载比例和计算资源分配，建立了一个系统能耗最小化问题。最后，采用结合了遗传算法算子的粒子群优化算法联合优化了无人机轨迹与计算卸载方案。仿真结果表明，所提算法具有良好的收敛性，可以使系统能耗降低约33.5%，具有较好的性能。

Abstract

As a new type of network architecture

the space-air-ground integrated network can effectively improve the network coverage and service quality

and is the key support for the realization of the Internet of everything in the future 6G. Addressing the issues of inefficient computational resource allocation and underutilization of unmanned aerial vehicle(UAV) within such networks

a joint trajectory optimization and computation offloading strategy was proposed for the space-air-ground integrated network. Firstly

the correlation relationship between the UAV's hovering model and the ground terminal was analyzed. Secondly

a system energy minimization problem was established by jointly considering the UAV trajectory

the matching factor between the UAV and the ground terminal

the task offloading ratio and the computational resource allocation. Finally

a particle swarm optimization algorithm combined with a genetic algorithm operator was used to jointly optimize the UAV trajectory and the computational offloading scheme. The simulation results show that the proposed algorithm has good convergence and can reduce the system energy consumption by about 33.5% with good performance.

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Keywords

references

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