Imbalance aware ensemble learning and multi domain enable optical transceiver fault diagnosis for 5G 6G and IoT transport networks

Imbalance aware ensemble learning and multi domain enable optical transceiver fault diagnosis for 5G 6G and IoT transport networks | 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 Imbalance aware ensemble learning and multi domain enable optical transceiver fault diagnosis for 5G 6G and IoT transport networks Mohammed Al-Hubaishi, Mustafa Avaz, Murat Sait Dogan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8406388/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 Optical transceivers are a critical building block of high-capacity transport networks that support 5G/6G and large-scale IoT services. Their in-service telemetry is noisy, high-dimensional, and strongly imbalanced toward normal operation, which complicates early fault detection. This paper presents an imbalance-aware ensemble learning framework coupled with multi-domain feature engineering for optical transceiver health classification and fault diagnosis. Using the IEEE DataPort Optical Transceiver Extracted Data dataset, we construct 187 features spanning statistical summaries, temporal trends, frequency-domain descriptors, wavelet coefficients, and physics-inspired indicators derived from power and temperature. We evaluate Random Forest, XGBoost, support vector machines, and gradient boosting, together with a weighted-vote ensemble, under stratified 80:20 and 60:40 train-test splits. Combining SMOTE with class weighting improves the minority-class F1-score from 0.72 to 0.93, while the ensemble achieves 97.1% accuracy with AUC 0.993 on the 80:20 split and 95.6% accuracy with AUC 0.986 on the 60:40 split. Ablation experiments show that the proposed feature engineering pipeline yields a 9.4-point absolute accuracy gain. Feature-importance analysis identifies optical power deviation and temperature-power coupling as key discriminators. The median inference time is 4.2 ms per sample for individual models and 12--15 ms per sample for the ensemble, enabling near real-time monitoring. These results support proactive maintenance and improved service reliability in next-generation networks. Optical transceivers fault diagnosis machine learning ensemble learning feature engineering class imbalance XGBoost 5G 6G transport predictive maintenance 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. 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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-8406388","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":569260748,"identity":"39bd82a0-e474-413e-bd6a-646314e51745","order_by":0,"name":"Mohammed Al-Hubaishi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYFACHjDJuEGCsfEBiMtHipZmAxCXjQQtDGwSIBZBLbrtvQc/3cyxkd0u3dxW+TXHToaNgfnhoxt4tJidOZcsnbstzXjnnINtt2W3JQMdxmZsnINPy40cA6CWw4kbbiS23ZbcxgzUwsMmTUCL8e/cbf/BWoolt9UTpcUMaMsBsBbGj9sOE6HlzBkz69xtycYb7hxslmbcdpyHjZmQX473GN/O3WYnu+F2+8OPP7dV2/OzNz98jE8LCmAGxxEzscpBgPEHKapHwSgYBaNgxAAAfepNvQUHGuIAAAAASUVORK5CYII=","orcid":"","institution":"Assistant Professor, Department of Computer Engineering, Halic University","correspondingAuthor":true,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"Al-Hubaishi","suffix":""},{"id":569260750,"identity":"7292283e-750a-411f-b10e-1829c0500ab3","order_by":1,"name":"Mustafa Avaz","email":"","orcid":"","institution":"UG Scholar, Department of Computer Engineering, Halic University","correspondingAuthor":false,"prefix":"","firstName":"Mustafa","middleName":"","lastName":"Avaz","suffix":""},{"id":569260753,"identity":"a76b64a8-a57f-4154-a2f4-247f2d117d15","order_by":2,"name":"Murat Sait Dogan","email":"","orcid":"","institution":"UG Scholar, Department of Computer Engineering, Halic University","correspondingAuthor":false,"prefix":"","firstName":"Murat","middleName":"Sait","lastName":"Dogan","suffix":""}],"badges":[],"createdAt":"2025-12-19 15:38:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8406388/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8406388/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99612939,"identity":"975232b6-733f-4714-891f-abb49ae1851c","added_by":"auto","created_at":"2026-01-06 12:51:39","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5489,"visible":true,"origin":"","legend":"","description":"","filename":"9a8903d898ff43069e737ec1fb74d203.json","url":"https://assets-eu.researchsquare.com/files/rs-8406388/v1/ff86c5cd2b638143e913bc6a.json"},{"id":102772491,"identity":"caa9b901-464f-48b1-9679-e6d598eddb9b","added_by":"auto","created_at":"2026-02-16 12:56:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":424914,"visible":true,"origin":"","legend":"","description":"","filename":"imbalanceawareensemblelearningOpticalProjectspringer.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8406388/v1_covered_1617448b-ddff-49a4-8a0b-6e5fc96dcce2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Imbalance aware ensemble learning and multi domain enable optical transceiver fault diagnosis for 5G 6G and IoT transport networks","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":"Optical transceivers, fault diagnosis, machine learning, ensemble learning, feature engineering, class imbalance, XGBoost, 5G 6G transport, predictive maintenance","lastPublishedDoi":"10.21203/rs.3.rs-8406388/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8406388/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Optical transceivers are a critical building block of high-capacity transport networks that support 5G/6G and large-scale IoT services. Their in-service telemetry is noisy, high-dimensional, and strongly imbalanced toward normal operation, which complicates early fault detection. This paper presents an imbalance-aware ensemble learning framework coupled with multi-domain feature engineering for optical transceiver health classification and fault diagnosis. Using the IEEE DataPort Optical Transceiver Extracted Data dataset, we construct 187 features spanning statistical summaries, temporal trends, frequency-domain descriptors, wavelet coefficients, and physics-inspired indicators derived from power and temperature. We evaluate Random Forest, XGBoost, support vector machines, and gradient boosting, together with a weighted-vote ensemble, under stratified 80:20 and 60:40 train-test splits. Combining SMOTE with class weighting improves the minority-class F1-score from 0.72 to 0.93, while the ensemble achieves 97.1% accuracy with AUC 0.993 on the 80:20 split and 95.6% accuracy with AUC 0.986 on the 60:40 split. Ablation experiments show that the proposed feature engineering pipeline yields a 9.4-point absolute accuracy gain. Feature-importance analysis identifies optical power deviation and temperature-power coupling as key discriminators. The median inference time is 4.2 ms per sample for individual models and 12--15 ms per sample for the ensemble, enabling near real-time monitoring. 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