计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (11): 270-279.doi: 10.11896/jsjkx.241100163

• 计算机网络 • 上一篇    下一篇

SCDDA:基于SCA和Dinkelbach的空-天-地一体化网络无人机轨迹与计算卸载优化方法

郑晶晶1,2,3, 陈星1,2,3, 张建山4   

  1. 1 福州大学计算机与大数据学院 福州 350116
    2 大数据智能教育部工程研究中心 福州 350002
    3 福建省网络计算与智能信息处理重点实验室 福州 350116
    4 闽江学院计算机与大数据学院 福州 350116
  • 收稿日期:2024-11-27 修回日期:2025-03-16 出版日期:2025-11-15 发布日期:2025-11-06
  • 通讯作者: 张建山(jszhang@mju.edu.cn)
  • 作者简介:(1518699263@qq.com)
  • 基金资助:
    国家自然科学基金(62072108);福建省自然科学基金(2024J08277);福建省促进海洋与渔业产业高质量发展专项(FJHYF-ZH-2023-02);福建省技术创新重点攻关及产业化项目(2024XQ004)

SCDDA:SCA and Dinkelbach-based Approach for UAV Trajectory and Computation Offloading in Space-Air-Ground Integrated Networks

ZHENG Jingjing1,2,3, CHEN Xing1,2,3, ZHANG Jianshan4   

  1. 1 College of Computer and Data Science,Fuzhou University,Fuzhou 350116,China
    2 Engineering Research Center of Big Data Intelligence,Ministry of Education,Fuzhou 350002,China
    3 Fujian Key Laboratory of Network Computing and Intelligent Information Processing,Fuzhou 350116,China
    4 School of Computer and Big Data,Minjiang University,Fuzhou 350116,China
  • Received:2024-11-27 Revised:2025-03-16 Online:2025-11-15 Published:2025-11-06
  • About author:ZHENG Jingjing,born in 1999,postgraduate,is a student member of CCF(No.W5869G).Her main research interests include mobile edge computing and space-air-ground integrated networks.
    ZHANG Jianshan,born in 1995,Ph.D,associate professor,is a distinguished member of CCF(No.D2738M).His main research interests include space-air-ground integrated networks and edge intelligence.
  • Supported by:
    National Natural Science Foundation of China(62072108),Natural Science Foundation of Fujian Province(2024J08277),Special Funds for Promoting High-quality Development of Marine and Fishery Industries in Fujian Province(FJHYF-ZH-2023-02)and Fujian Key Technological Innovation and Industrialization Projects(2024XQ004).

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摘要： 移动设备的普及所产生的海量异构数据,对数据通信网络提出了更高的要求。在此背景下,第六代移动网络(6G)有望满足各种移动设备执行计算密集型和延迟敏感型移动应用的需求。当前,空间、空中和地面的网络组件之间有机结合所形成的新颖的空-天-地一体化网络(SAGIN)成为6G架构的关键组成部分。与传统地面通信范式相比,SAGIN可以利用卫星、高空平台和无人机等非地面网络组件有效扩大移动通信网络的覆盖范围和提高吞吐量,可以很好地满足无基础设施地区中大量移动设备的需求。尽管SAGIN在无基础设施地区中各方面的应用潜力巨大,但其仍面临资源限制、网络拓扑动态变化和移动设备服务要求等实际挑战。针对上述挑战,考虑了真实场景下用户移动性对系统能效的影响,研究了一种SAGIN中的计算卸载和无人机(UAV)路径规划联合优化问题。为解决目标联合优化问题,基于凸优化技术设计了一种高效新颖的算法,将目标问题解耦成两个子问题,并分别通过逐次凸逼近(SCA)和 Dinkelbach 方法求解子问题,以得到目标优化问题的近似最优解。数值仿真结果表明,与其他基准算法相比,所提算法表现出的性能更优。

关键词: 空-天-地一体化网络, 无人机轨迹, 计算卸载, 用户移动性

Abstract: The massive amount of heterogeneous data generated by the widespread use of mobile devices has placed higher demands on data communication networks.In this context,the sixth-generation(6G) mobile network is expected to meet the needs of various mobile devices executing computation-intensive and latency-sensitive mobile applications.Currently,the novel Space-Air-Ground Integrated Network(SAGIN),which results from the organic combination of network components in space,air,and ground,has become a key component of the 6G architecture.Compared with traditional terrestrial communication paradigms,SAGIN can effectively enhance the coverage and throughput of mobile communication networks by utilizing non-terrestrial network components such as satellites,high-altitude platforms,and UAVs.This makes it well-suited to meet the needs of a large number of mobile devices in infrastructure-less areas.Despite the significant potential of SAGIN in various aspects for infrastructure-less regions,its application still faces practical challenges such as resource constraints,dynamic changes in network topology,and service requirements of mobile devices.To address these challenges,this paper considers the impact of user mobility on system energy efficiency under real-world scenarios,and investigates a joint optimization problem of computation offloading and UAV trajectory in SAGIN.To solve the targeted joint optimization problem,an efficient and novel algorithm based on convex optimization techniques is designed,decoupling the target problem into two sub-problems.These sub-problems are solved separately using the SCA and the Dinkelbach method,to obtain an approximate optimal solution to the target optimization problem.Numerical simulation results demonstrate that the proposed algorithm outperforms other benchmark algorithms in terms of performance.

Key words: Space-Air-Ground Integrated Network, UAV trajectory, Computation offloading, User mobility

中图分类号: 

  • TP399
[1]ZHU X,JIANG C.Integrated satellite-terrestrial networks toward 6G:Architectures,applications,and challenges[J].IEEE Internet of Things Journal,2022,9(1):437-461.
[2]TAN J,TANG F,ZHAO M,et al.Performance analysis ofspace-air-ground integrated network(SAGIN):UAV altitude and position angle[C]//2023 IEEE/CIC International Confe-rence on Communications in China(ICCC).2023:1-6.
[3]MAO Y,YOU C,ZHANG J,et al.A survey on mobile edge computing:The communication perspective[J].IEEE Communications Surveys Tutorials,2017,19(4):2322-2358.
[4]WU J,JIA M,GUO Q.Space-Air-Ground Integrated Network Architecture Based on Mobile Edge Computing[J].Space-Integrated-Ground Information Networks,202,5(1):24-31.
[5]SUN S,ZHANG G,MEI H,et al.Optimizing multi-UAV deployment in 3D space to minimize task completion time in UAV-enabled mobile edge computing systems[J].IEEE Communications Letters,2021,25(2):579-583.
[6]WANG Y,RU Z Y,WANG K,et al.Joint deployment and task scheduling optimization for large-scale mobile users in multi-UAV-enabled mobile edge computing[J].IEEE Transactions on Cybernetics,2020,50(9):3984-3997.
[7]ZHOU F,WU Y,HU R Q,et al.Computation rate maximiza-tion in UAV-enabled wireless-powered mobile-edge computing systems[J].IEEE Journal on Selected Areas in Communications,2018,36(9):1927-1941.
[8]ZHOU F,WU Y,SUN H,et al.UAV-enabled mobile edge computing:Offloading optimization and trajectory design[C]//2018 IEEE International Conference on Communications(ICC).2018:1-6.
[9]LI M,CHENG N,GAO J,et al.Energy-efficient UAV-assisted mobile edge computing:Resource allocation and trajectory optimization[J].IEEE Transa8ctions on Vehicular Technology,2020,69(3):3424-3438.
[10]WANG D,TIAN J,ZHANG H,et al.Task offloading and tra-jectory scheduling for UAV-enabled MEC networks:An optimal transport theory perspective[J].IEEE Wireless Communications Letters,2022,11(1):150-154.
[11]XIANG K,HE Y.UAV-assisted MEC system considering UAVtrajectory and task offloading strategy[C]//ICC 2023－IEEE International Conference on Communications.2023:4677-4682.
[12]CHEN J,XING H,XIAO Z,et al.A DRL agent for jointly optimizing computation offloading and resource allocation in MEC[J].IEEE Internet of Things Journal,2021,8(24):508-524.
[13]ZHU B,BEDEER E,NGUYEN H H,et al.UAV trajectoryplanning in wireless sensor networks for energy consumption minimization by deep reinforcement learning[J].IEEE Transactions on Vehicular Technology,2021,70(9):9540-9554.
[14]LIU Q,SHI L,SUN L,et al.Path planning for UAV-mounted mobile edge computing with deep reinforcement learning[J].IEEE Transactions on Vehicular Technology,2020,69(5):5723-5728.
[15]WAN S,LU J,FAN P,et al.Toward big data processing inIOT:Path planning and resource management of UAV base stations in mobile-edge computing system[J].IEEE Internet of Things Journal,2020,7(7):5995-6009.
[16]WANG Z,YU H,ZHU S,et al.Curriculum reinforcement lear-ning-based computation offloading approach in space-air-ground integrated network[C]//2021 13th International Conference on Wireless Communications and Signal Processing(WCSP).2021:1-6.
[17]LI X Q,HE Z Q,ZHOU W T.Task Offloading and Resource Allocation for the MEC-Enabled Integrated Satellite-Terrestrial Network,[J].Computer Applications and Software,2023,40(2):130-137.
[18]ZHOU C,WU W,HE H,et al.Deep reinforcement learning for delay-oriented IOT task scheduling in SAGIN[J].IEEE Tran-sactions on Wireless Communications,2021,20(2):911-925.
[19]YANG Z Y,BI S Z,ZHANG Y J A.Online trajectory and resource optimization for stochastic UAV-enabled MEC systems[J].IEEE Transactions on Wireless Communications,2022,21(7):5629-5643.
[20]MIAO Y,HWANG K,WU D,et al.Drone swarm path planning for mobile edge computing in industrial internet of things[J].IEEE Transactions on Industrial Informatics,2023,19(5):6836-6848.
[21]LI Q,SHI L,ZHANG Z,et al.Resource allocation in UAV-enabled wireless-powered MEC networks with hybrid passive and active communications[J].IEEE Internet of Things Journal,2023,10(3):2574-2588.
[22]EL-EMARY M,RANJHA A,NABOULSI D,et al.Energy-efficient task offloading and trajectory design for UAV-based MEC systems[C]//2023 19th International Conference on Wireless and Mobile Computing,Networking and Communications(WiMob).2023:274-279.
[23]DINKELBACH W.On nonlinear fractional programming[J].Management Science,1967,13(7):492-498.
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