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A Collaborative Task Scheduling Method for Heterogeneous LEO Satellite Networks Based on Mixed-strategy Game

2026-03-17Documento de conferência Acesso embargado
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dc.contributor.authorNiu, Qingyuan
dc.contributor.authorTeng, Haojun
dc.contributor.authorJin, Shilong
dc.contributor.authorWang, Yingjie
dc.contributor.authorHou, Wenhan
dc.contributor.authorLiu, Cong
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.date.accessioned2026-05-14T14:27:01Z
dc.date.available2026-05-14T14:27:01Z
dc.date.embargoedUntil2028-03-17
dc.date.issued2026-03-17
dc.descriptionNiu, Q., Teng, H., Jin, S., Wang, Y., Hou, W., & Liu, C. (2026). A Collaborative Task Scheduling Method for Heterogeneous LEO Satellite Networks Based on Mixed-strategy Game. In 2025 International Conference on Satellite Computing, Satellite 2025 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/Satellite67108.2025.11430429
dc.description.abstractWith the rapid deployment of Low Earth Orbit (LEO) satellite constellations and the increasing demand for satellite-ground collaborative computing, efficient resource management and low-latency processing have become pressing challenges. This paper proposes a novel collaborative planning algorithm, the Evolutionary Reward Function (ERF), which integrates mixed-strategy game theory with Deep Reinforcement Learning (DRL). ERF models the interactive decision-making among heterogeneous satellite nodes through mixed-strategy games and incorporates a dynamic policy adjustment mechanism to enhance task scheduling efficiency, thereby improving mission completion rates in dynamic LEO environments. Extensive simulation results demonstrate that ERF outperforms conventional DRL methods in terms of convergence speed and overall performance.en
dc.description.versionauthorsversion
dc.description.versionpublished
dc.format.extent6
dc.format.extent785531
dc.identifier.doi10.1109/Satellite67108.2025.11430429
dc.identifier.isbn979-8-3315-8111-4
dc.identifier.isbn979-8-3315-8110-7
dc.identifier.otherPURE: 162877605
dc.identifier.otherPURE UUID: f27db7bf-52d0-42f6-b4ae-4762c8ee1970
dc.identifier.otherScopus: 105037859714
dc.identifier.otherWOS: 001795111600005
dc.identifier.urihttp://hdl.handle.net/10362/203083
dc.identifier.urlhttps://www.scopus.com/pages/publications/105037859714
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001795111600005
dc.language.isoeng
dc.peerreviewedyes
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectCollaborative task scheduling
dc.subjectDeep reinforcement learning
dc.subjectLEO satellite networks
dc.subjectMixed-strategy game theory
dc.subjectAerospace Engineering
dc.subjectComputer Networks and Communications
dc.subjectHardware and Architecture
dc.subjectSDG 9 - Industry, Innovation, and Infrastructure
dc.titleA Collaborative Task Scheduling Method for Heterogeneous LEO Satellite Networks Based on Mixed-strategy Gameen
dc.typeconference object
degois.publication.title2025 International Conference on Satellite Computing, Satellite 2025
degois.publication.title2025 International Conference on Satellite Computing
dspace.entity.typePublication
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