大规模卫星网络通算协同资源管理

鲍晨曦; 盛敏; 周笛; 姬思敬; 史琰; 李建东

doi:10.12263/DZXB.20250885

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大规模卫星网络通算协同资源管理

中国电子学会科学技术奖特约专栏 | 更新时间：2026-04-24

- 大规模卫星网络通算协同资源管理
- Communication and Computing Collaborative Resource Management for Large-Scale Satellite Networks
- 电子学报 2025年53卷第12期页码：4199-4215
- 作者机构：
  
  西安电子科技大学空天地一体化综合业务网全国重点实验室，陕西西安 710071
- 作者简介：
  
  [ "鲍晨曦女，1998年11月出生于黑龙江省佳木斯市.现为西安电子科技大学通信工程学院博士研究生.主要研究方向为卫星网络资源表征、大规模卫星网络资源管控技术.E-mail: cxbao@stu.xidian.edu.cn" ]
  [ "盛敏女，1975年8月出生于湖南省长沙市.现为西安电子科技大学教授、博士生导师、空天地一体化综合业务网全国重点实验室主任.主要研究方向为空地融合网络、智能无线网络和移动自组织网.E-mail: msheng@mail.xidian.edu.cn" ]
  [ "周笛女，1991年3月出生于陕西省西安市.现为西安电子科技大学教授、博士生导师.主要研究方向为空地融合网络、空间信息网络中的动态资源分配、任务规划和性能评估.中国电子学会会员编号：E190023060S.E-mail: zhoudi@xidian.edu.cn" ]
  [ "姬思敬男，1993年7月出生于河南省郑州市.现为西安电子科技大学副研究员.主要研究方向为大规模卫星星座组网理论、星地通算协同技术.E-mail: jisijing@xidian.edu.cn" ]
  [ "史琰男，1975年1月出生于河南省洛阳市.现为西安电子科技大学教授、博士生导师.主要研究方向为空间信息网络、高性能通信与计算协同.E-mail: yshi@xidian.edu.cn" ]
  [ "李建东男，1962年10月出生于江苏省盐城市.现为西安电子科技大学教授、博士生导师.中国电子学会会士.主要研究方向为空间信息网络、智能无线网络、大规模自组织网络.中国电子学会会员编号：E190012560F.E-mail: jdli@mail.xidian.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(62461160329;62422114;62371360)
- DOI：10.12263/DZXB.20250885
  中图分类号： TN927.23;
- 收稿：2025-09-30，
  
  录用：2025-11-25，
  
  纸质出版：2025-12-25
- 稿件说明：
移动端阅览
鲍晨曦, 盛敏, 周笛, 等. 大规模卫星网络通算协同资源管理[J]. 电子学报, 2025, 53(12): 4199-4215.

BAO Chen-xi, SHENG Min, ZHOU Di, et al. Communication and Computing Collaborative Resource Management for Large-Scale Satellite Networks[J]. Acta Electronica Sinica, 2025, 53(12): 4199-4215.
鲍晨曦, 盛敏, 周笛, 等. 大规模卫星网络通算协同资源管理[J]. 电子学报, 2025, 53(12): 4199-4215. DOI：10.12263/DZXB.20250885

BAO Chen-xi, SHENG Min, ZHOU Di, et al. Communication and Computing Collaborative Resource Management for Large-Scale Satellite Networks[J]. Acta Electronica Sinica, 2025, 53(12): 4199-4215. DOI：10.12263/DZXB.20250885

摘要

大规模卫星网络（Large-scale Satellite Networks，LSN）星载算力的显著提升，推动了星上自主资源管理的实现，是保障多样化业务端到端（End-to-End，E2E）服务时效性的关键手段.然而，LSN拓扑的高动态变化使得星间通算资源难以高效协同，对满足各类业务差异化的时效性需求并确保高质量E2E服务提出了严峻挑战.为此，本文通过建立虚拟节点及链路映射模型，形成静态覆盖业务请求的虚拟网络，有效避免卫星高速运动对业务E2E服务的影响；通过建立融合LSN拓扑结构的网络状态信息提取模型，实时捕捉星间结构化通算资源与业务需求特征的动态演化关系，并通过设计参数共享的切片资源分配决策机制，实现星地协作智能切片资源管理及融合业务需求特征的网络通算资源切片；此外，通过设计区域资源管理模式并引入服务导向信息，为星上自主E2E服务决策提供局部拓扑感知和目标定位能力，实现动态LSN通算资源的按需高效协同，借助星载处理能力改善时延约束下多样化业务的E2E服务性能.仿真结果表明，提出的算法与非拓扑感知算法相比，在不同通算资源及业务到达数量下分别可提升28%、25.2%和39.3%的服务完成性能.

Abstract

The significant improvement in onboard computing capabilities of large-scale satellite networks (LSNs) has facilitated the realization of satellite autonomous resource management

which is a key means to ensure the timeliness of end-to-end (E2E) services for diversified services. However

the highly dynamic topology of LSNs makes it difficult to efficiently collaborate on inter-satellite communication and computing resources

posing severe challenges to meeting the differentiated timeliness requirements of various services and ensuring high-quality E2E services. To this end

this paper establishes a virtual node and link mapping model to form a virtual network that statically covers service requests

effectively avoiding the impact of high-speed satellite movement on E2E services. Furthermore

a network status information extraction model is designed that integrates the LSN topology

which captures the dynamic evolution relationship of inter-satellite structured communication and computing resources and service demand characteristics in real time. Leveraging a parameter-sharing slice resource allocation decision-making mechanism

satellite-ground collaborative intelligent slice resource management and network communication and computing resource slicing with service demand characteristics can be achieved. In addition

by designing a regional resource management mode and introducing service orientation information

local topology awareness and target positioning capabilities are provided for satellite autonomous E2E service decision-making. It achieves on-demand and efficient coordination of dynamic communication and computing resources

leveraging onboard processing capabilities to improve the E2E service performance of diversified services under latency constraints. Simulation results show that the proposed algorithm can improve the service completion performance by 28%

25.2% and 39.3% respectively

under different communication and computing resources and service request numbers compared with the non-topology-aware algorithm.

关键词

Keywords

references

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