Hybrid Deep Learning-Based Optimization for Efficient 6G Free-Space Optical Communication

Hybrid Deep Learning-Based Optimization for Efficient 6G Free-Space Optical Communication | 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 Hybrid Deep Learning-Based Optimization for Efficient 6G Free-Space Optical Communication kuppani sathish, Samula Annapurna, Vamsi Krishna Reddy Bandaru, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6739402/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 With the introduction of 6G wireless communication, it is necessary to provide ultra-high reliability, high capacity and super-low latency. Free-Space Optical (FSO) solutions are main contributors to this. The changing weather conditions in the air decrease the performance of light signals sent by FSO. We recommend a new system that merges Social Spider Optimisation and Waterwheel Plant Optimisation to ensure that the U-net architecture provides robust signal detection under turbulent conditions in FSO channels. Instead of conventional or mixed designs, ours matches network frameworks to optical channel perturbations, allowing the network to resist changes in environmental conditions. Results from multiple simulation tests show that BER, latency, spectral efficiency and capacity all improve considerably, reaching 25 bps/Hz, 1 ms and 10⁶ linked users. This method supports the IEEE 6G viewpoint and prepares the way for dependable, broadly applied FSO systems for defense, surveillance and remote sensing. 6G Free-Space Optical Communication Hybrid Deep Learning Signal Detection Atmospheric Turbulence U-Net Optimization Algorithm Bit Error Rate Full Text 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-6739402","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":473043664,"identity":"59d2a00e-b818-4def-bae8-f764a63b5d2d","order_by":0,"name":"kuppani sathish","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYDACHijFDyITCkjRItkA0mJAghYGgwNgkggd8j1nzB4X1NyRMT6/OvHDAwMGeX6xA/i1GJztMTeecewZj9mNt5slgA4znDk7gYAWfh4zaR62w0AtZzeAtCQY3CagRb4fpOXfYR7jGWc3/yBKC8PZHjNp3rbDPAb8vduIs8XgzLFy45l9h3kkbvBus0gwkCDsF/me5G2PC74dtufvP7v55o8KG3l+aUIOY2BgYwZTEmCVEgSVI2nhP0CU6lEwCkbBKBiBAADxakDNL/kBEQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-4651-8310","institution":"Tirumala Engineering college","correspondingAuthor":true,"prefix":"","firstName":"kuppani","middleName":"","lastName":"sathish","suffix":""},{"id":473043665,"identity":"0e41ec4c-bb55-4ad5-8d36-37f10164b36b","order_by":1,"name":"Samula Annapurna","email":"","orcid":"","institution":"Vishnu Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Samula","middleName":"","lastName":"Annapurna","suffix":""},{"id":473043666,"identity":"7383c8a1-63c3-4474-893a-809ef2b74db0","order_by":2,"name":"Vamsi Krishna Reddy Bandaru","email":"","orcid":"","institution":"Simplyturn technologies","correspondingAuthor":false,"prefix":"","firstName":"Vamsi","middleName":"Krishna Reddy","lastName":"Bandaru","suffix":""},{"id":473043667,"identity":"43a72a5f-64dd-4cb5-a33c-a95ce2c46262","order_by":3,"name":"Monica Bhutani","email":"","orcid":"","institution":"Bharathi vidyapeet college of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Monica","middleName":"","lastName":"Bhutani","suffix":""}],"badges":[],"createdAt":"2025-05-24 13:47:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6739402/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6739402/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109296249,"identity":"bcbabfcb-23e4-401d-b369-2cefda95b904","added_by":"auto","created_at":"2026-05-15 08:46:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1658220,"visible":true,"origin":"","legend":"","description":"","filename":"APSCI6v46G1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6739402/v1_covered_a9504f97-93ea-42ce-98af-f9a70d295e18.pdf"}],"financialInterests":"","formattedTitle":"Hybrid Deep Learning-Based Optimization for Efficient 6G Free-Space Optical Communication","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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, Free-Space Optical Communication, Hybrid Deep Learning, Signal Detection, Atmospheric Turbulence, U-Net, Optimization Algorithm, Bit Error Rate","lastPublishedDoi":"10.21203/rs.3.rs-6739402/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6739402/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the introduction of 6G wireless communication, it is necessary to provide ultra-high reliability, high capacity and super-low latency. Free-Space Optical (FSO) solutions are main contributors to this. The changing weather conditions in the air decrease the performance of light signals sent by FSO. We recommend a new system that merges Social Spider Optimisation and Waterwheel Plant Optimisation to ensure that the U-net architecture provides robust signal detection under turbulent conditions in FSO channels. Instead of conventional or mixed designs, ours matches network frameworks to optical channel perturbations, allowing the network to resist changes in environmental conditions. Results from multiple simulation tests show that BER, latency, spectral efficiency and capacity all improve considerably, reaching 25 bps/Hz, 1 ms and 10⁶ linked users. 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