Autonomous Resource Orchestration for 6G Space-Air-Ground Networks: A Self- Supervised Learning Approach

Autonomous Resource Orchestration for 6G Space-Air-Ground Networks: A Self- Supervised Learning Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Autonomous Resource Orchestration for 6G Space-Air-Ground Networks: A Self- Supervised Learning Approach Zacheous Aasa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8829064/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The realization of sixth generation (6G) wireless networks heavily relies on incorporating space-air-ground integrated network (SAGIN) capabilities to realize global ubiquity. However, the inherent stochasticity of non-terrestrial network (NTN) node mobility, in conjunction with terrestrial resource variability, creates a multidimensional orchestration problem. Traditional optimization and supervised learning schemes are often prohibitive due to high computational complexity and the extreme cost of data labeling. This manuscript therefore presents a novel autonomous resource orchestration scheme with the application of self-supervised learning. By learning efficient spatial-temporal patterns, the scheme supports optimized power allocation together with beamforming for RIS-assisted wireless links. Extensive simulations for the application of the scheme confirm near-optimal sum-rate performance with 40% lower data requirements when compared with standard deep reinforcement learning schemes. 6G Communications Autonomous Resource Orchestration Edge Intelligence Self-Supervised Learning Space Air Ground-Integrated Networks SAGIN Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8829064","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":592081183,"identity":"b63edc9a-c5d3-412a-85bb-bf1bc31098a2","order_by":0,"name":"Zacheous Aasa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYHACNiR2BRAzMzeQouUMSAsjKVoY28Akfi3y7YefPfhQcdiegf3w4w8/59VG87cDtfyo2IZTC2NPmrnhjDOHExt40swke7cdz51xmLGBsefMbZxamBly2KR52w4nMDAkmDHwbjuW2wDUwszYhlsLG/8bNum/bUCH8T///PHvnGO58wlp4ZEA2sLYBlQmkWMgzdtQk7uBkBYJiWdmkj1n0hPbJN6UScscO5C7EajlID6/yPcnP5P4UWFtz8+fvvnjm5q63HnnDx988KMCtxaEpyDUYTB5gLB6BKgjRfEoGAWjYBSMEAAAvS9W0AOCaloAAAAASUVORK5CYII=","orcid":"","institution":"Ladoke Akintola University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Zacheous","middleName":"","lastName":"Aasa","suffix":""}],"badges":[],"createdAt":"2026-02-09 10:23:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8829064/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8829064/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104974176,"identity":"d8a0366c-4183-4d4f-bd54-9e2bed94da32","added_by":"auto","created_at":"2026-03-19 11:42:18","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":523659,"visible":true,"origin":"","legend":"","description":"","filename":"AasaZacheousSAGINSSLManuscript202984676wed.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8829064/v1_covered_bd2aa3d1-f57a-4ccb-b022-1f1db0ca226b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Autonomous Resource Orchestration for 6G Space-Air-Ground Networks: A Self- Supervised Learning Approach","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"6G Communications, Autonomous Resource Orchestration, Edge Intelligence, Self-Supervised Learning, Space Air Ground-Integrated Networks, SAGIN","lastPublishedDoi":"10.21203/rs.3.rs-8829064/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8829064/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The realization of sixth generation (6G) wireless networks heavily relies on incorporating space-air-ground integrated network (SAGIN) capabilities to realize global ubiquity. 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