Prediction of Channel Propagation Impairments in LEO Satellite Networks Using Machine Learning

Prediction of Channel Propagation Impairments in LEO Satellite Networks Using Machine Learning | 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 Prediction of Channel Propagation Impairments in LEO Satellite Networks Using Machine Learning Salma Ashraf Elsayed, Michael Naiem Abdelmassih Ibrahim, Hadia Saeid Elhennawy, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8499178/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract This paper introduces a transformative multi-task spatiotemporal deep learning framework designed for zero-shot geographic generalization in predictive channel modeling for Low Earth Orbit (LEO) satellite communications at Q-band (39 GHz). Unlike conventional methods that necessitate location-specific training, the coordinate-based Long Short-Term Memory (LSTM) architecture processes historical sequences of atmospheric and geographic data spanning 60 minutes to simultaneously predict weather conditions and Excess Path Loss (EPL) for 5 hours, incorporating inherent Gaussian uncertainty quantification. The proposed framework effectively addresses the significant limitation of geographic generalization that has been a challenge for previous machine learning approaches in satellite communications. A comprehensive evaluation conducted across various European climates demonstrates exceptional dual performance, achieving a Root Mean Square Error (RMSE) at trained locations and a notable average error across ten entirely unseen European cities, representing a substantial improvement over existing methods. The framework sustains an average prediction accuracy across all untrained European cities, with leading locations such as Berlin attaining high accuracy. The architecture exhibits a high degree of uncertainty calibration reliability and facilitates real-time deployment without the need for location-specific retraining, thereby establishing a new paradigm for global-scale predictive link adaptation in extensive LEO constellations. LEO satellite communications Q-band propagation deep learning-based channel prediction Long Short-Term Memory networks multi-task LSTM atmospheric impairment forecasting atmospheric attenuation modeling Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 13 Mar, 2026 Reviewers invited by journal 13 Mar, 2026 Editor assigned by journal 15 Jan, 2026 First submitted to journal 15 Jan, 2026 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-8499178","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":605622317,"identity":"bf5a0fdc-22d0-4ea9-a61a-4785e81d5116","order_by":0,"name":"Salma Ashraf Elsayed","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIie3OvWrDMBDA8RMCZVHwKlOavsKVjC34VS4U4sWBjB1dCvaSpGsCfYj2DRS0GtLRo710ytDRKe6HvHlR8Fio/oOQBD/uAHy+P1jA2UNF97eT3h+dJ2HODVbFfDqc4EHMwzozs3QwASNRkeDx00X+Bk1rIBglCB+fbsEeJSJJsdg9F0u2zgyEqyOy3cZNOJdIpOTipUwIxqkBLBPk45WbCEs0oYojS1hrF4s68nWGSC5mKRF2SnMp7BRlCTRuojg3QJqut2UC5jKLpSrel/t16ibRYZ+fTt8/V8E2rutjezMJ8rvXqmndpL8j6u7srpplg8io6j2GTfH5fL7/0S8vFlS1DeFJqwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0004-9979-8282","institution":"Ain Shams University Faculty of Engineering","correspondingAuthor":true,"prefix":"","firstName":"Salma","middleName":"Ashraf","lastName":"Elsayed","suffix":""},{"id":605622318,"identity":"27bb315f-3eae-42b8-b8f6-4dd5efaef601","order_by":1,"name":"Michael Naiem Abdelmassih Ibrahim","email":"","orcid":"","institution":"Ain Shams University Faculty of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"Naiem Abdelmassih","lastName":"Ibrahim","suffix":""},{"id":605622319,"identity":"2e3ee345-4a4d-4069-b8b5-0a3784a00414","order_by":2,"name":"Hadia Saeid Elhennawy","email":"","orcid":"","institution":"Ain Shams University Faculty of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Hadia","middleName":"Saeid","lastName":"Elhennawy","suffix":""},{"id":605622320,"identity":"abffa0a0-b456-4854-a625-bfb773a33adb","order_by":3,"name":"Eman Salah Abass Gad","email":"","orcid":"","institution":"Ain Shams University Faculty of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Eman","middleName":"Salah Abass","lastName":"Gad","suffix":""}],"badges":[],"createdAt":"2026-01-02 08:16:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8499178/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8499178/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104683951,"identity":"156c6b1e-2edf-4f89-88a7-28a03f91f7db","added_by":"auto","created_at":"2026-03-16 03:40:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":981541,"visible":true,"origin":"","legend":"","description":"","filename":"Predictionofchannelpropagartionimpairments.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8499178/v1_covered_6f609bd9-235c-4092-988d-ee9a852da679.pdf"}],"financialInterests":"","formattedTitle":"Prediction of Channel Propagation Impairments in LEO Satellite Networks Using Machine Learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"eurasip-journal-on-wireless-communications-and-networking","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jwcn","sideBox":"Learn more about [EURASIP Journal on Wireless Communications and Networking](http://jwcn-eurasipjournals.springeropen.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jwcn/default.aspx","title":"EURASIP Journal on Wireless Communications and Networking","twitterHandle":"@SpringerEng","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"LEO satellite communications, Q-band propagation, deep learning-based channel prediction, Long Short-Term Memory networks, multi-task LSTM, atmospheric impairment forecasting, atmospheric attenuation modeling","lastPublishedDoi":"10.21203/rs.3.rs-8499178/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8499178/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This paper introduces a transformative multi-task spatiotemporal deep learning framework designed for zero-shot geographic generalization in predictive channel modeling for Low Earth Orbit (LEO) satellite communications at Q-band (39 GHz). Unlike conventional methods that necessitate location-specific training, the coordinate-based Long Short-Term Memory (LSTM) architecture processes historical sequences of atmospheric and geographic data spanning 60 minutes to simultaneously predict weather conditions and Excess Path Loss (EPL) for 5 hours, incorporating inherent Gaussian uncertainty quantification. The proposed framework effectively addresses the significant limitation of geographic generalization that has been a challenge for previous machine learning approaches in satellite communications. A comprehensive evaluation conducted across various European climates demonstrates exceptional dual performance, achieving a Root Mean Square Error\n(RMSE) at trained locations and a notable average error across ten entirely unseen European cities, representing a substantial improvement over existing methods. 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