Adaptive Cognitive Control of Multi-domain Unmanned Systems Under Adversarial Swarm Influence

Adaptive Cognitive Control of Multi-domain Unmanned Systems Under Adversarial Swarm Influence | 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 Adaptive Cognitive Control of Multi-domain Unmanned Systems Under Adversarial Swarm Influence Serhii Liventsev This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9596627/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 This paper presents a novel adaptive cognitive control framework for multi-domain unmanned systems (UAVs, USVs, UGVs) operating under coordinated adversarial swarm jamming. Unlike existing approaches that focus on isolated trajectory or routing optimization, the proposed method jointly manages communication parameters, network topology, iterative LDPC decoding, and anti-jamming strategies within a unified cognitive loop. A hybrid state-space model captures the evolution of communication quality, energy state, topology survivability, and mission constraints. The control problem is cast as a minimax optimization, where the friendly swarm reconfigures its SDR-RAN architecture to guarantee performance under worst-case interference. A hysteresis-based topology switching mechanism prevents oscillatory reconfiguration (ping-pong effect). Using Lyapunov and input-to-state stability (ISS) analysis, we prove that the closed-loop system remains bounded for bounded adversarial disturbances. Simulation results over a realistic multi-domain scenario (12 UAVs, 6 USVs, 8 UGVs) demonstrate that the proposed cognitive control reduces the bit error rate by up to an order of magnitude at SINR = − 5 dB, improves topology survivablity by 40 % under coordinated swarm jamming, and lowers the average number of LDPC decoding iteratios by 25–35 % compared to fixed-iteration schemes, while maintaining computational feasibility. These findings confirm that integrated cognitive adaptation of communication, decoding, and topology is essential for resilient operation in hostile electromagnetic environments. Cell Communication and Signaling Robotics cognitive control multi-domain unmanned systems SDR-RAN LDPC codes minimax adaptation anti-jamming swarm resilience topology reconfiguration Full Text Additional Declarations The authors declare no competing interests. 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9596627","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633476276,"identity":"e3f585b4-8649-49de-8836-91727ac79f22","order_by":0,"name":"Serhii Liventsev","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYFAC5oYDUBbjwwYGCWK0MIK1gJQyGxKthQGqhU2ygRgN8g2MjYd5Ku7Uybf3mFXO3GHBYHD8AH4tBgcYGw7znHkmYXDmjNnNjWckGAzOJBDQAnTYwZlthyUMJNLSbj5sA2o5QEAL0GFALf8OS8jPf5ZWCNZy/gEBzwAdduBjw2EJhhvMxxg3grTcIOSww0AtH44dltxwJvmw5MwzEjySNwjYIt/efPhDQs1hfvn2g40fe3fUyfGdJ2ALAzMyBxhHPATUowNItI6CUTAKRsEoQAUA6UpKN50fjLMAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-9768-8133","institution":"National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”,","correspondingAuthor":true,"prefix":"","firstName":"Serhii","middleName":"","lastName":"Liventsev","suffix":""}],"badges":[],"createdAt":"2026-05-02 23:36:06","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-9596627/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9596627/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108582595,"identity":"3e44e8dd-a249-4396-8429-b65e638da32c","added_by":"auto","created_at":"2026-05-06 08:20:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":791504,"visible":true,"origin":"","legend":"","description":"","filename":"ADAPTIVECOGNITIVECONTROLOFMULTI.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9596627/v1_covered_4d8f93fb-b01e-4ac1-8e8c-cf0b4de2da18.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAdaptive Cognitive Control of Multi-domain Unmanned Systems Under Adversarial Swarm Influence\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”","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":"cognitive control, multi-domain unmanned systems, SDR-RAN, LDPC codes, minimax adaptation, anti-jamming, swarm resilience, topology reconfiguration","lastPublishedDoi":"10.21203/rs.3.rs-9596627/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9596627/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper presents a novel adaptive cognitive control framework for multi-domain unmanned systems (UAVs, USVs, UGVs) operating under coordinated adversarial swarm jamming. Unlike existing approaches that focus on isolated trajectory or routing optimization, the proposed method jointly manages communication parameters, network topology, iterative LDPC decoding, and anti-jamming strategies within a unified cognitive loop. A hybrid state-space model captures the evolution of communication quality, energy state, topology survivability, and mission constraints. The control problem is cast as a minimax optimization, where the friendly swarm reconfigures its SDR-RAN architecture to guarantee performance under worst-case interference. A hysteresis-based topology switching mechanism prevents oscillatory reconfiguration (ping-pong effect). Using Lyapunov and input-to-state stability (ISS) analysis, we prove that the closed-loop system remains bounded for bounded adversarial disturbances. Simulation results over a realistic multi-domain scenario (12 UAVs, 6 USVs, 8 UGVs) demonstrate that the proposed cognitive control reduces the bit error rate by up to an order of magnitude at SINR = \u0026minus;\u0026thinsp;5 dB, improves topology survivablity by 40 % under coordinated swarm jamming, and lowers the average number of LDPC decoding iteratios by 25\u0026ndash;35 % compared to fixed-iteration schemes, while maintaining computational feasibility. 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