高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

面向任务驱动的动态可伸缩空间信息网络架构设计与优化

何立军 贾子晔 李世银 汪彦婷 王丽 刘磊

何立军, 贾子晔, 李世银, 汪彦婷, 王丽, 刘磊. 面向任务驱动的动态可伸缩空间信息网络架构设计与优化[J]. 电子与信息学报. doi: 10.11999/JEIT240505
引用本文: 何立军, 贾子晔, 李世银, 汪彦婷, 王丽, 刘磊. 面向任务驱动的动态可伸缩空间信息网络架构设计与优化[J]. 电子与信息学报. doi: 10.11999/JEIT240505
HE Lijun, JIA Ziye, LI Shiyin, WANG Yanting, WANG Li, LIU Lei. Design and Optimization of Task-driven Dynamic Scalable Network Architecture in Spatial Information Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240505
Citation: HE Lijun, JIA Ziye, LI Shiyin, WANG Yanting, WANG Li, LIU Lei. Design and Optimization of Task-driven Dynamic Scalable Network Architecture in Spatial Information Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240505

面向任务驱动的动态可伸缩空间信息网络架构设计与优化

doi: 10.11999/JEIT240505
基金项目: 国家自然科学基金(62201463, 61901388),江苏省自然科学基金(BK20220883)
详细信息
    作者简介:

    何立军:男,副教授,博士,研究方向为空天地一体化网络、无人机组网、无线通信

    贾子晔:女,副教授,博士,研究方向为低空智联网、无人机、空天地一体化网络

    李世银:男,教授,博士,研究方向为无线通信、可见光通信定位一体化、智能感知与精确定位、矿山互联网、语义通信

    汪彦婷:女,讲师,博士,研究方向为边缘计算、边缘计算、群智网络、模型剪枝

    王丽:女,讲师,博士,研究方向为移动通信网络、物联网软件技术、多源异构信息融合

    刘磊:男,讲师,博士,研究方向为无线网络性能分析、非正交多址接入、干扰管理技术

    通讯作者:

    李世银 lishiyin@cumt.edu.cn

  • 中图分类号: TN929.5

Design and Optimization of Task-driven Dynamic Scalable Network Architecture in Spatial Information Networks

Funds: The National Natural Science Foundation of China (62201463, 61901388), The Natural Science Foundation of Jiangsu Province of China (BK20220883)
  • 摘要: 现阶段空间信息网络中各卫星子系统各成体系且相互割裂,使得网络呈现封闭、分裂态势,形成严峻资源壁垒,造成空间资源协同应用能力弱以及网络扩展能力低等难题。传统架构设计采用对现阶段空间网络架构的“完全颠覆”的思路,大大增加了实际部署的难度。为此,该文立足于卫星网络现状,采取“按步骤分阶段升级”的思路,促进现有网络架构的演进,从任务驱动角度开展动态可伸缩空间信息网络架构模型研究,实现空间资源在各卫星子系统间高效动态共享,促进空间资源根据任务需求变化而动态高效汇聚。首先,提出分阶段实现的网络架构模型,旨在兼容和升级现有网络架构。随后,介绍核心部件网络资源协调器的详细设计,包括网络结构与工作协议、超帧结构以及高效的网络资源动态分配策略,实现空间数据的高效传输。仿真结果表明,所提网络架构实现了网络资源高效共享,大大提升空间信息网络的网络性能。
  • 图  1  天地协同网络架构示意图

    图  2  网络架构分阶段演进

    图  3  网络资源协调器网络结构与工作流程设计

    图  4  基于任务驱动的超帧结构

    图  5  网络模型结构示意图

    图  6  网络卸载数据量随控制变量$ V $的收敛过程

    图  7  网络性能随时隙的变化趋势

    图  8  网络卸载数量随卫星天线发射功率的变化趋势

    图  9  不同方案下的网络性能比较

    表  1  基于任务驱动的动态可伸缩空间信息网络架构

     (1)信息输入:初始化队列$ {\boldsymbol{Q}}(t) = \{ {Q_n}(t)\} $
     (2)资源感知:资源协调器在超帧$ t $内的时隙0:1)收集物理网络信息计算卫星与数传站之间的时间窗口信息;2)收集卸载任务请求,结合时间窗口信息,构建卸载任务信息;
     (3)算法执行:在超帧$ t $的时隙1内:
       (a)构建优化问题$ {\text{P6}} $,调用Kuhn-Munkres 算法求解最优变量值$ {{\boldsymbol{x}}^{\rm{opt}}} $;
       (b)基于最优变量值$ {{\boldsymbol{x}}^{\rm{opt}}} $,构建优化问题$ {\text{P8}} $,并执行如下流程求解最优变量值$ ({y}^{\text{opt}},{f}^{\text{opt}}) $,其中$ {y}^{\text{opt}}=\left\{({y}_{s,n}^{t}{)}^{\text{opt}}\right\} $和$ {f}^{\text{opt}}=\left\{({f}_{s}^{t}{)}^{\text{opt}}\right\} $;
       For $ s = 1:S $
        根据式(14)求出 $ ({y}_{s,n}^{t}{)}^{\text{opt}} $;
         $ ({f}_{s}^{t}{)}^{\text{opt}}={{\displaystyle \sum _{h\in \mathcal{H}}({x}_{s,h}^{t})}}^{\text{opt}}{C}_{s,h}^{t} $
       End
     (4)策略分发:资源协调器将$ {{\boldsymbol{x}}^{\rm{opt}}} $和$ ({y}^{\text{opt}},{f}^{\text{opt}}) $转化为指令,推送到实际卫星子系统,进而构建虚拟数传站;
     (5)数据卸载:虚拟卫星子系统根据其网络资源和任务信息执行数据卸载;
     (6)超帧索引更新:检测超帧$ t $的时长是否执行结束,若结束则更新超帧索引:$ t = t + 1 $,根据式(5)更新队列$ {\boldsymbol{Q}}(t) $,并返回步骤(2).
    下载: 导出CSV
  • [1] HU Fei, YANG Chaowei, SCHNASE J L, et al. Climatespark: An in-memory distributed computing framework for big climate data analytics[J]. Computers & Geosciences, 2018, 115: 154–166. doi: 10.1016/j.cageo.2018.03.011.
    [2] LI Deren.On the space and sky information real-time intelligent service system with deep integration of military and civilian[J].Civil-Military Integration on Cyberspace, 2018(12):12-15.

    LI Deren.On the space and sky information real-time intelligent service system with deep integration of military and civilian[J].Civil-Military Integration on Cyberspace, 2018(12):12-15.
    [3] 张威, 张更新, 苟亮. 空间信息网络中的星座设计方法研究[J]. 中兴通讯技术, 2016, 22(4): 19–23,45. doi: 10.3969/j.issn.1009-6868.2016.04.004.

    ZHANG Wei, ZHANG Gengxin, and GOU Liang. Satellite constellation design in space information network[J]. ZTE Technology Journal, 2016, 22(4): 19–23,45. doi: 10.3969/j.issn.1009-6868.2016.04.004.
    [4] ZHANG Wei, ZHANG Gengxin, XIE Zhidong, et al. A hierarchical autonomous system based space information network architecture and topology control[J]. Journal of Communications and Information Networks, 2016, 1(3): 77–89. doi: 10.11959/j.issn.2096-1081.2016.017.
    [5] SUN Jiayu, QI Weijing, SONG Qingyang, et al. A new architecture for space information networks based on an MEO constellation optical backbone network[C]. Asia Communications and Photonics Conference 2017, Guangzhou, China, 2017: Su3C. 3. doi: 10.1364/ACPC.2017.Su3C.3.
    [6] MA Ting, QIAN Bo, QIN Xiaohan, et al. Satellite-terrestrial integrated 6G: An ultra-dense LEO networking management architecture[J]. IEEE Wireless Communications, 2024, 31(1): 62–69. doi: 10.1109/MWC.011.2200198.
    [7] ZHENG Gao, WANG Ning, and TAFAZOLLI R R. SDN in space: A virtual data-plane addressing scheme for supporting LEO satellite and terrestrial networks integration[J]. IEEE/ACM Transactions on Networking, 2024, 32(2): 1781–1796. doi: 10.1109/TNET.2023.3330672.
    [8] YANG Huiting, LIU Wei, WANG Xiangfeng, et al. Group sparse space information network with joint virtual network function deployment and maximum flow routing strategy[J]. IEEE Transactions on Wireless Communications, 2023, 22(8): 5291–5305. doi: 10.1109/TWC.2022.3233067.
    [9] XIA Qiufen, WANG Guijie, XU Zichuan, et al. Efficient algorithms for service chaining in NFV-enabled satellite edge networks[J]. IEEE Transactions on Mobile Computing, 2024, 23(5): 5677–5694. doi: 10.1109/TMC.2023.3312352.
    [10] BAO Jinzhen, ZHAO Baokang, YU Wangrong, et al. OpenSAN: A software-defined satellite network architecture[C]. Proceedings of the 2014 ACM Conference on SIGCOMM, Chicago, USA, 2014: 347–348. doi: 10.1145/2619239.2631454.
    [11] YANG Bowei, WU Yue, CHU Xiaoli, et al. Seamless handover in software-defined satellite networking[J]. IEEE Communications Letters, 2016, 20(9): 1768–1771. doi: 10.1109/LCOMM.2016.2585482.
    [12] FENG Jing, JIANG Lei, SHEN Ye, et al. A scheme for software defined ORS satellite networking[C]. 2014 IEEE Fourth International Conference on Big Data and Cloud Computing, Sydney, Australia, 2014: 716–721. doi: 10.1109/BDCloud.2014.19.
    [13] FENG Bohao, ZHOU Huachun, ZHANG Hongke, et al. HetNet: A flexible architecture for heterogeneous satellite-terrestrial networks[J]. IEEE Network, 2017, 31(6): 86–92. doi: 10.1109/MNET.2017.1600330.
    [14] SHENG Min, WANG Yu, LI Jiandong, et al. Toward a flexible and reconfigurable broadband satellite network: Resource management architecture and strategies[J]. IEEE Wireless Communications, 2017, 24(4): 127–133. doi: 10.1109/MWC.2017.1600173.
    [15] LI Taixin, ZHOU Huachun, LUO Hongbin, et al. SERvICE: A software defined framework for integrated space-terrestrial satellite communication[J]. IEEE Transactions on Mobile Computing, 2018, 17(3): 703–716. doi: 10.1109/TMC.2017.2732343.
    [16] ZHANG Ning, ZHANG Shan, YANG Peng, et al. Software defined space-air-ground integrated vehicular networks: Challenges and solutions[J]. IEEE Communications Magazine, 2017, 55(7): 101–109. doi: 10.1109/MCOM.2017.1601156.
    [17] SHI Yongpeng, CAO Yurui, LIU Jiajia, et al. A cross-domain SDN architecture for multi-layered space-terrestrial integrated networks[J]. IEEE Network, 2019, 33(1): 29–35. doi: 10.1109/MNET.2018.1800191.
    [18] CHEN Chen, LIAO Zhan, JU Ying, et al. Hierarchical domain-based multicontroller deployment strategy in SDN-enabled space-air-ground integrated network[J]. IEEE Transactions on Aerospace and Electronic Systems, 2022, 58(6): 4864–4879. doi: 10.1109/TAES.2022.3199191.
    [19] ESMAT H H, LORENZO B, and SHI Weisong. Toward resilient network slicing for satellite-terrestrial edge computing IoT[J]. IEEE Internet of Things Journal, 2023, 10(16): 14621–14645. doi: 10.1109/JIOT.2023.3277466.
    [20] CHENG Nan, LYU Feng, QUAN Wei, et al. Space/aerial-assisted computing offloading for IoT applications: A learning-based approach[J]. IEEE Journal on Selected Areas in Communications, 2019, 37(5): 1117–1129. doi: 10.1109/JSAC.2019.2906789.
    [21] ZHANG Zhenjiang, ZHANG Wenyu, and TSENG F H. Satellite mobile edge computing: Improving QoS of high-speed satellite-terrestrial networks using edge computing techniques[J]. IEEE Network, 2019, 33(1): 70–76. doi: 10.1109/MNET.2018.1800172.
    [22] QIU Chao, YAO Haipeng, YU R F, et al. Deep Q-learning aided networking, caching, and computing resources allocation in software-defined satellite-terrestrial networks[J]. IEEE Transactions on Vehicular Technology, 2019, 68(6): 5871–5883. doi: 10.1109/TVT.2019.2907682.
    [23] XIE Renchao, TANG Qinqin, WANG Qiuning, et al. Satellite-terrestrial integrated edge computing networks: Architecture, challenges, and open issues[J]. IEEE Network, 2020, 34(3): 224–231. doi: 10.1109/MNET.011.1900369.
    [24] ZHANG Yalin, GAO Xiaozheng, YUAN Hang, et al. Joint UAV trajectory and power allocation with hybrid FSO/RF for secure space-air-ground communications[J]. IEEE Internet of Things Journal, 2024, 11(19): 31407–31421. doi: 10.1109/JIOT.2024.3419264.
    [25] ZHAI Daosen, ZHANG Ruonan, DU Jianbo, et al. Simultaneous wireless information and power transfer at 5G new frequencies: Channel measurement and network design[J]. IEEE Journal on Selected Areas in Communications, 2019, 37(1): 171–186. doi: 10.1109/JSAC.2018.2872366.
    [26] SONG Yanjie, OU Junwei, WU Jian, et al. A cluster-based genetic optimization method for satellite range scheduling system[J]. Swarm and Evolutionary Computation, 2023, 79: 101316. doi: 10.1016/j.swevo.2023.101316.
  • 加载中
图(9) / 表(1)
计量
  • 文章访问数:  64
  • HTML全文浏览量:  24
  • PDF下载量:  11
  • 被引次数: 0
出版历程
  • 收稿日期:  2024-06-19
  • 修回日期:  2024-11-17
  • 网络出版日期:  2024-11-29

目录

    /

    返回文章
    返回