Communication and Computing Collaborative Resource Management for Large-Scale Satellite Networks

BAO Chen-xi; SHENG Min; ZHOU Di; JI Si-jing; SHI Yan; LI Jian-dong

doi:10.12263/DZXB.20250885

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Communication and Computing Collaborative Resource Management for Large-Scale Satellite Networks

Special Issue\: Recipients of CIE Science and Technology Awards | 更新时间：2026-04-24

- Communication and Computing Collaborative Resource Management for Large-Scale Satellite Networks
- ACTA ELECTRONICA SINICA Vol. 53, Issue 12, Pages: 4199-4215(2025)
- 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：
- 基金信息：
  
  National Natural Science Foundation of China(62461160329;62422114;62371360)
- DOI：10.12263/DZXB.20250885
  CLC： TN927.23;
- Received：30 September 2025，
  
  Accepted：25 November 2025，
  
  Published：25 December 2025
- 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

WANG C X , YOU X H , GAO X Q , et al . On the road to 6G: Visions, requirements, key technologies and testbeds [J ] . IEEE Communications Surveys & Tutorials , 2023 , 25 ( 2 ): 905 - 974 .

DING F , BAO C X , ZHOU D , et al . Toward autonomous resource management architecture for 6G satellite-terrestrial integrated networks [J ] . IEEE Network , 2024 , 38 ( 2 ): 113 - 121 .

缪德山 , 邓凌越 , 孙建成 , 等 . 6G星地融合无线网络及关键技术 [J ] . 中兴通讯技术 , 2024 , 30 ( 4 ): 42 - 49 .

MIAO D S , DENG L Y , SUN J C , et al . Terrestrial and satellite network integration based 6G system and its key technologies [J ] . ZTE Technology Journal , 2024 , 30 ( 4 ): 42 - 49 . (in Chinese)

SAAD W , BENNIS M , CHEN M Z . A vision of 6G wireless systems: Applications, trends, technologies, and open research problems [J ] . IEEE Network , 2020 , 34 ( 3 ): 134 - 142 .

QU Y B , ZHANG T T , FENG Y , et al . Computation offloading and resource allocation for E2E tasks in satellite edge computing networks [J ] . Space: Science & Technology , 2024 , 4 : 144 .

SHEN X , LIAO W R , YIN Q . A novel wireless resource management for the 6G-enabled high-density Internet of Things [J ] . IEEE Wireless Communications , 2022 , 29 ( 1 ): 32 - 39 .

AL HOMSSI B , DAKIC K , WANG K , et al . Artificial intelligence techniques for next-generation massive satellite networks [J ] . IEEE Communications Magazine , 2024 , 62 ( 4 ): 66 - 72 .

ZHOU D , SHENG M , LI J D , et al . Aerospace integrated networks innovation for empowering 6G: A survey and future challenges [J ] . IEEE Communications Surveys & Tutorials , 2023 , 25 ( 2 ): 975 - 1019 .

CIANCA E , ROSSI T , RUGGIERI M , et al . Space sustainability-by-design: Challenges and key-enablers [J ] . IEEE Aerospace and Electronic Systems Magazine , 2025 , 40 ( 4 ): 4 - 17 .

WEI F S , FENG G , SUN Y , et al . Network slice reconfiguration by exploiting deep reinforcement learning with large action space [J ] . IEEE Transactions on Network and Service Management , 2020 , 17 ( 4 ): 2197 - 2211 .

RAFIQUE W , RANI BARAI J , FAPOJUWO A O , et al . A survey on beyond 5G network slicing for smart cities applications [J ] . IEEE Communications Surveys & Tutorials , 2025 , 27 ( 1 ): 595 - 628 .

EBRAHIMI S , BOUALI F , HAAS O C L . Resource management from single-domain 5G to end-to-end 6G network slicing: A survey [J ] . IEEE Communications Surveys & Tutorials , 2024 , 26 ( 4 ): 2836 - 2866 .

CHEN Q , GUO Z , MENG W X , et al . A survey on resource management in joint communication and computing-embedded SAGIN [J ] . IEEE Communications Surveys & Tutorials , 2025 , 27 ( 3 ): 1911 - 1954 .

ALHASHIMI H F , HINDIA M N , DIMYATI K , et al . A survey on resource management for 6G heterogeneous networks: Current research, future trends, and challenges [J ] . Electronics , 2023 , 12 ( 3 ): 647 .

LIANG H , YANG Z Q , ZHANG G B , et al . Resource allocation for space-air-ground integrated networks: A comprehensive review [J ] . Journal of Communications and Information Networks , 2024 , 9 ( 1 ): 1 - 23 .

ZHANG S H , CUI G F , LONG Y T , et al . Joint computing and communication resource allocation for satellite communication networks with edge computing [J ] . China Communications , 2021 , 18 ( 7 ): 236 - 252 .

OUYANG Q L , YE N , GAO J , et al . Joint in-orbit computation and communication for minimizing download time from LEO satellites [J ] . IEEE Transactions on Mobile Computing , 2024 , 23 ( 5 ): 3950 - 3963 .

VALENTE F , ERAMO V , LAVACCA F G . Optimal bandwidth and computing resource allocation in low earth orbit satellite constellation for earth observation applications [J ] . Computer Networks , 2023 , 232 : 109849 .

SUI X F , JIANG Z Q , LYU Y F , et al . Integrating convex optimization and deep learning for downlink resource allocation in LEO satellites networks [J ] . IEEE Transactions on Cognitive Communications and Networking , 2024 , 10 ( 3 ): 1104 - 1118 .

ZHANG H Y , LIU R K , KAUSHIK A , et al . Satellite edge computing with collaborative computation offloading: An intelligent deep deterministic policy gradient approach [J ] . IEEE Internet of Things Journal , 2023 , 10 ( 10 ): 9092 - 9107 .

WANG D , BAI Y L , HUANG G , et al . Cache-aided MEC for IoT: Resource allocation using deep graph reinforcement learning [J ] . IEEE Internet of Things Journal , 2023 , 10 ( 13 ): 11486 - 11496 .

ZHANG H Y , ZHAO H B , LIU R K , et al . Dynamic user association and computation offloading in satellite edge computing networks via deep reinforcement learning [J ] . IEEE Transactions on Green Communications and Networking , 2024 , 8 ( 4 ): 1888 - 1901 .

WU H Q , CHEN J Y , ZHOU C H , et al . Learning-based joint resource slicing and scheduling in space-terrestrial integrated vehicular networks [J ] . Journal of Communications and Information Networks , 2021 , 6 ( 3 ): 208 - 223 .

LUO J , XIAO R Y , SUN P , et al . VLEO satellite radio access network slicing: A hierarchical deep reinforcement learning approach [C ] // 2023 IEEE Intl Conf on Parallel & Distributed Processing with Applications , Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking . Piscataway : IEEE , 2024 : 229 - 236 .

LIU Y R , MA T , TANG Z X , et al . Ultra-dense LEO satellite access network slicing: A deep reinforcement learning approach [C ] // GLOBECOM 2023 - 2023 IEEE Global Communications Conference . Piscataway : IEEE , 2024 : 5043 - 5048 .

LIU Y R , MA T , QIN X H , et al . Reconfigurable RAN slicing for ultra-dense LEO satellite networks via DRL [J ] . IEEE Transactions on Cognitive Communications and Networking , 2025 , 11 ( 1 ): 566 - 580 .

DONG T J , ZHUANG Z R , QI Q , et al . Intelligent joint network slicing and routing via GCN-powered multi-task deep reinforcement learning [J ] . IEEE Transactions on Cognitive Communications and Networking , 2022 , 8 ( 2 ): 1269 - 1286 .

KAK A , AKYILDIZ I F . Towards automatic network slicing for the Internet of space things [J ] . IEEE Transactions on Network and Service Management , 2022 , 19 ( 1 ): 392 - 412 .

ZHU Q Y , TAO H C , CAO Y H , et al . Laser inter-satellite link visibility and topology optimization for mega constellation [J ] . Electronics , 2022 , 11 ( 14 ): 2232 .

CHEN Q , GIAMBENE G , YANG L , et al . Analysis of inter-satellite link paths for LEO mega-constellation networks [J ] . IEEE Transactions on Vehicular Technology , 2021 , 70 ( 3 ): 2743 - 2755 .

SONG C L , HE S B , CUI Y H . Topological design of low orbit mega-constellations based on inter satellite visibility [C ] // Proceedings of the 2024 3rd International Conference on Cryptography, Network Security and Communication Technology . New York : ACM , 2024 : 560 - 564 .

WANG G C , ZHOU S , ZHANG S , et al . SFC-based service provisioning for reconfigurable space-air-ground integrated networks [J ] . IEEE Journal on Selected Areas in Communications , 2020 , 38 ( 7 ): 1478 - 1489 .

LASO FERNÁNDEZ J L . Study, modelling and design of intersatellite links (ISL) in millimeter-wave band/estudio, modelado y diseño de enlaces intersatelitales (ISL) en bandas de milimétricas [EB/OL ] . [ 2025-10-10 ] . https://oa.upm.es/62930/1/PFC_JOSE_LUIS_LASO_FERNANDEZ.pdf https://oa.upm.es/62930/1/PFC_JOSE_LUIS_LASO_FERNANDEZ.pdf .

HAARNOJA T , ZHOU A , ABBEEL P , et al . Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor [EB/OL ] . ( 2018-08-08 )[ 2025-10-10 ] . https://arXiv.org/abs/1801.01290 https://arXiv.org/abs/1801.01290 .

BAO C X , ZHOU D , SHENG M , et al . Regional resource management for service provisioning in LEO satellite networks: A topology feature-based DRL approach [C ] // 2025 IEEE Global Communications Conference (GLOBECOM) . Piscataway : IEEE , 2025 : 1 - 6 .

BHATTACHERJEE D , SINGLA A . Network topology design at 27 000 km/hour [C ] // Proceedings of the 15th International Conference on Emerging Networking Experiments and Technologies . New York : ACM , 2019 : 341 - 354 .

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