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

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

1.
中国矿业大学信息与控制工程学院徐州 221116
2.
南京航天航空大学电子信息工程学院南京 210024
3.
西北工业大学软件学院西安 710072
4.
西安邮电大学通信与信息工程学院西安 710121

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
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- 文章访问数: 36
- HTML全文浏览量: 9
- PDF下载量: 5
- 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

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

HE Lijun¹,
JIA Ziya²,
LI Shiyin^{1
, ,},
WANG Yanting³,
WANG Li³,
LIU Lei⁴

1.
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
2.
School of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210000, China
3.
School of Software, Northwestern Polytechnical University, Xi’an 710072, China
4.
School of Electronic and Communication Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China

Funds: The National Natural Science Foundation of China (62201463, 61901388), The Natural Science Foundation of Jiangsu Province of China (BK20220883)

摘要

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。
- 空间信息网络 /
- 网络架构 /
- 资源分配 /
- 数据传输
Abstract: At the present stage, the satellite subsystems in Space Information Networks (SINs) have their own systems and are separated from each other, which makes the network appear closed and fragmented, forming a severe resource barrier and resulting in weak collaborative application ability of space resources and low network expansion ability. The traditional architecture design adopts the “completely subversive” idea of the current space networks, which greatly increases the difficulty of actual deployment. Therefore, based on the current status of satellite networks, the idea of “upgrading step by step” is adopted to promote the evolution of the existing network architecture, and a dynamic and scalable architecture model is proposed in SINs from the perspective of mission drive, so as to realize the efficient and dynamic sharing of space resources among subsystems and promote the dynamic and efficient aggregation of space resources according to the changes in mission requirements. Firstly, a phased network architecture model is proposed, aiming at compatibility and upgrading of the existing network architecture. Then, the design of the core component coordinator is introduced, including network structure and working protocol, superframe structure, and efficient network resource allocation strategy, to realize the efficient transmission of spatial data. The simulation results show that the proposed network architecture realizes the efficient sharing of network resources and greatly improves the utilization rate of network resources.
- Spatial information networks /
- Network architecture /
- Resource allocation /
- Data transmission

HTML全文

图 1 天地协同网络架构示意图

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图 2 网络架构分阶段演进

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图 3 网络资源协调器网络结构与工作流程设计

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图 4 基于任务驱动的超帧结构

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图 5 网络模型结构示意图

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图 6 网络卸载数据量随控制变量$ V $的收敛过程

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图 7 网络性能随时隙的变化趋势

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图 8 网络卸载数量随卫星天线发射功率的变化趋势

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图 9 不同方案下的网络性能比较

(a) 网络卸载数量 (b) 队列平均长度

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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}}^{{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

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