Performance Degradation of Static Fusion under Stochastic Resonance: Revisiting the SSFW Method

Performance Degradation of Static Fusion under Stochastic Resonance: Revisiting the SSFW Method | 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 Article Performance Degradation of Static Fusion under Stochastic Resonance: Revisiting the SSFW Method Hasan Serdar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7637977/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 study revisits the Static Spectrum Fusion Weighting (SSFW) method for spectrum sensing under stochastic resonance (SR) conditions. While SSFW has shown efficiency in stable noise environments, its performance degrades significantly under nonstationary noise perturbations introduced by SR. This paper proposes an extension to the SSFW method by integrating adaptive fusion weight recalibration and dynamic thresholds based on real-time noise estimates. Simulation results show that the revised SSFW method outperforms traditional fusion schemes, such as PSO and RSA, under varying levels of SR noise variance, achieving up to 20% higher detection probability. We demonstrate the robustness of the proposed method and highlight the critical role of adaptive fusion strategies in maintaining performance in stochastic environments. Physical sciences/Engineering Physical sciences/Mathematics and computing Physical sciences/Physics 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. 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-7637977","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":516392573,"identity":"d5327678-53b7-4386-8243-d795fa20229b","order_by":0,"name":"Hasan Serdar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYBACAxDBw8AgByIZG8BiCcRpMUbVcoAILYkNRGsx5z987MObmnvp/dJnD36cUXOHgZ89x4D54x7cWixnpCXPnHOsOHdmX16y5IZjzxgke94YMBx4hsdhN3iMmXnYEnI3nOExkHzAdhgokgPUgsdlBufPf2bm+ZeQbn+Gx/jng3+HGewJajmQw8zM25aQYMDDYya5sQ1oiwQhLTfSjBnn9iUYzjjDl2Y5s+8wj8SZZwUHzuB12OHHDG++Jcjz9/Aevtnz7bAcf3vyxgcVeLRgAB4QQYqGUTAKRsEoGAVYAAB1FlahkbejugAAAABJRU5ErkJggg==","orcid":"","institution":"Necmettin Erbakan University","correspondingAuthor":true,"prefix":"","firstName":"Hasan","middleName":"","lastName":"Serdar","suffix":""}],"badges":[],"createdAt":"2025-09-17 08:53:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7637977/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7637977/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92684478,"identity":"6d4f84cf-5de5-4340-b1b0-a0cc07bb06be","added_by":"auto","created_at":"2025-10-03 02:31:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":423321,"visible":true,"origin":"","legend":"","description":"","filename":"TemplateforsubmissionstoScientificReports1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7637977/v1_covered_c30f3f01-57d3-44a2-9670-64724cc70aa9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Performance Degradation of Static Fusion under Stochastic Resonance: Revisiting the SSFW Method","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":"","lastPublishedDoi":"10.21203/rs.3.rs-7637977/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7637977/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This study revisits the Static Spectrum Fusion Weighting (SSFW) method for spectrum sensing under stochastic resonance (SR) conditions. While SSFW has shown efficiency in stable noise environments, its performance degrades significantly under nonstationary noise perturbations introduced by SR. This paper proposes an extension to the SSFW method by integrating adaptive fusion weight recalibration and dynamic thresholds based on real-time noise estimates. Simulation results show that the revised SSFW method outperforms traditional fusion schemes, such as PSO and RSA, under varying levels of SR noise variance, achieving up to 20\\% higher detection probability. We demonstrate the robustness of the proposed method and highlight the critical role of adaptive fusion strategies in maintaining performance in stochastic environments.","manuscriptTitle":"Performance Degradation of Static Fusion under Stochastic Resonance: Revisiting the SSFW Method","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-18 02:52:05","doi":"10.21203/rs.3.rs-7637977/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"09f409ba-0a79-45f8-b487-80ec092284d9","owner":[],"postedDate":"September 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54863812,"name":"Physical sciences/Engineering"},{"id":54863813,"name":"Physical sciences/Mathematics and computing"},{"id":54863814,"name":"Physical sciences/Physics"}],"tags":[],"updatedAt":"2025-10-03T02:23:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-18 02:52:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7637977","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7637977","identity":"rs-7637977","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}