Hybrid Machine Learning based Optimization with Beamforming of 6G Mobile Radio Network for LEO Satellite base station in Urban Areas

Hybrid Machine Learning based Optimization with Beamforming of 6G Mobile Radio Network for LEO Satellite base station in Urban Areas | 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 Machine Learning based Optimization with Beamforming of 6G Mobile Radio Network for LEO Satellite base station in Urban Areas Haymanot Derebe Bizuneh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8089868/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 rapid evolution of sixth-generation (6G) mobile communication networks requires advanced optimization and intelligent deep learning models to meet the demands of ultra-reliable, low-latency, and energy-efficient wireless systems. Low Earth Orbit (LEO) satellites has a vital role in enabling non-terrestrial net works (NTN), particularly in dense urban environment areas. This paper presents a comparative analysis of hybrid optimization approaches integrating Artificial Bee Colony (ABC), Grey Wolf Optimizer (GWO), Ant Lion Optimizer (ALO), Whale Optimization Algorithm (WOA), Salp Swarm Algorithm (SSA), and Par ticle Swarm Optimization (PSO) with Extreme Learning Machine (ELM) for beamforming in 6G mobile radio networks. Integrated with beamforming for 6G NTN. The models are analysis using pathloss (PL),signal-to-interference plus-noise ratio (SINR), coverage area, capacity, spectral efficiency (SE), energy efficiency (EE), bandwidth efficiency (BE), bit error rate (BER), and propaga tion delay. ABC-ELM has performed with result mean values of the PL (187.46 dB), SINR (22.62 dB), coverage area (68.03%), capacity (10.82 Gbps), SE (10.82 bits/Hz), EE (44.10%), BE (44.10%), BER (1.45−8 bps ) and propagation delay (48.65%) in urban areas. As a result, demonstrating of hybrid ELM-based opti mization integrated with beamforming is a promising approach for enabling 6G LEO satellite networks. Artificial Intelligence and Machine Learning Cell Communication and Signaling Electrical Engineering Hybrid machine learning swarm intelligent algorithms beamforming low earth orbit satellite 6G NTN planning urban areas. 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. 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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-8089868","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":543532176,"identity":"3415708c-1c8a-4597-a9b5-3e910c4ed926","order_by":0,"name":"Haymanot Derebe Bizuneh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYDCCAwwMzGASAmyAmLHxAC7VWLQkpIG0NJCk5TBMEDfgO9778HFBxR15g2uHnz34+OO83dr2w0BbamyicWmRPHPc2HjGmWeGG26nmRvOSLidvO1MIlDLsbTcBhxaDG6ksUnzth1m3HA7wUyaB6jF7ABQC2PDYdxa7j9j/83777D9htvp34BaziWbnX9IQMsNNjZm3obDiRtu54BsOWBndoOALZJn0pilZxw7nDzzdk6Z5Iy05ASzG0BbEvD4he/4McbPBTWHbftup2+T+GBjZ292Pv3hgw81Nji1wIHCAQidCFaZQEg5CMhDDbUnRvEoGAWjYBSMLAAAAcBtU8mVRCEAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-2034-5810","institution":"Adama Science and Technology University","correspondingAuthor":true,"prefix":"","firstName":"Haymanot","middleName":"Derebe","lastName":"Bizuneh","suffix":""}],"badges":[],"createdAt":"2025-11-11 19:36:39","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-8089868/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8089868/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96239759,"identity":"73662138-d76a-44d0-ada2-b668016ace73","added_by":"auto","created_at":"2025-11-19 07:07:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3462508,"visible":true,"origin":"","legend":"","description":"","filename":"manuiscript21.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8089868/v1_covered_0053e8ba-9996-434f-92a3-780ed107f3da.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eHybrid Machine Learning based Optimization with Beamforming of 6G Mobile Radio Network for LEO Satellite base station in Urban Areas\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Adama Science and Technology University","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":" Hybrid machine learning, swarm intelligent algorithms, beamforming, low earth orbit satellite, 6G NTN planning, urban areas.","lastPublishedDoi":"10.21203/rs.3.rs-8089868/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8089868/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid evolution of sixth-generation (6G) mobile communication networks \u0026nbsp;requires advanced optimization and intelligent deep learning models to meet the \u0026nbsp;demands of ultra-reliable, low-latency, and energy-efficient wireless systems. Low \u0026nbsp;Earth Orbit (LEO) satellites has a vital role in enabling non-terrestrial net works (NTN), particularly in dense urban environment areas. This paper presents \u0026nbsp;a comparative analysis of hybrid optimization approaches integrating Artificial \u0026nbsp;Bee Colony (ABC), Grey Wolf Optimizer (GWO), Ant Lion Optimizer (ALO), \u0026nbsp;Whale Optimization Algorithm (WOA), Salp Swarm Algorithm (SSA), and Par ticle Swarm Optimization (PSO) with Extreme Learning Machine (ELM) for \u0026nbsp;beamforming in 6G mobile radio networks. Integrated with beamforming for \u0026nbsp;6G NTN. The models are analysis using pathloss (PL),signal-to-interference plus-noise ratio (SINR), coverage area, capacity, spectral efficiency (SE), energy \u0026nbsp;efficiency (EE), bandwidth efficiency (BE), bit error rate (BER), and propaga tion delay. ABC-ELM has performed with result mean values of the PL (187.46 \u0026nbsp;dB), SINR (22.62 dB), coverage area (68.03%), capacity (10.82 Gbps), SE (10.82 \u0026nbsp;bits/Hz), EE (44.10%), BE (44.10%), BER (1.45−8 bps ) and propagation delay (48.65%) in urban areas. As a result, demonstrating of hybrid ELM-based opti mization integrated with beamforming is a promising approach for enabling 6G \u0026nbsp;LEO satellite networks.\u003c/p\u003e","manuscriptTitle":"Hybrid Machine Learning based Optimization with Beamforming of 6G Mobile Radio Network for LEO Satellite base station in Urban Areas","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 12:46:13","doi":"10.21203/rs.3.rs-8089868/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":"fe3d9bf8-860d-447b-9c8c-f629696becea","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57825746,"name":"Artificial Intelligence and Machine Learning"},{"id":57825747,"name":"Cell Communication and Signaling"},{"id":57825748,"name":"Electrical Engineering"}],"tags":[],"updatedAt":"2025-11-13T12:46:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-13 12:46:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8089868","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8089868","identity":"rs-8089868","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}