Explainable Machine Learning for Path Loss Modeling in mmWave and Sub-THz Communication Systems

Explainable Machine Learning for Path Loss Modeling in mmWave and Sub-THz Communication Systems | 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 Explainable Machine Learning for Path Loss Modeling in mmWave and Sub-THz Communication Systems Samba Siva Reddy Mula, Vinay Kumar Kumar Pamula This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8693763/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 5 You are reading this latest preprint version Abstract Machine learning has emerged as a powerful tool for large-scale path loss modeling at millimeter-wave (mmWave) and sub-terahertz (sub-THz) frequencies. While recent studies demonstrate improved prediction accuracy, most learning based models operate as black boxes, limiting their adoption in deployment-critical wireless system design and stan dardization activities. This paper addresses this limitation by proposing an explainable machine learning framework for path loss modeling that emphasizes interpretability, physical consis tency, and engineering trust. Using multi-band mmWave and sub-THz measurement datasets, we analyze how key propa gation features—including transmitter–receiver separation dis tance, carrier frequency, antenna height, and line-of-sight condi tions—influence model predictions. Feature attribution and sen sitivity analyses are employed to quantify the relative importance of these parameters and to assess whether learned relationships align with established propagation theory. The results demon strate that the proposed models capture physically meaningful monotonic trends with respect to distance and frequency, while avoiding non-physical artifacts commonly observed in purely data-driven approaches. Rather than prioritizing marginal accu racy gains, this work highlights the tradeoff between predictive performance and model transparency, showing that interpretable models can achieve competitive accuracy while offering substan tially improved explainability. The presented framework provides actionable insights for network planning, site-specific modeling, and data-assisted calibration of analytical channel models. These findings support the role of explainable machine learning as a key enabler for trustworthy and deployable path loss modeling in future mmWave and sub-THz wireless communication systems. Explainable machine learning path loss modeling millimeter-wave communications sub-terahertz systems wireless propagation feature attribution model interpretability 6G networks Full Text Supplementary Files snarticle.tex Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Major revision 22 Apr, 2026 Reviewers agreed at journal 13 Mar, 2026 Reviewers invited by journal 13 Mar, 2026 Editor assigned by journal 11 Feb, 2026 First submitted to journal 10 Feb, 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-8693763","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":605625148,"identity":"a8545cd5-9481-4131-81fb-f8509d305451","order_by":0,"name":"Samba Siva Reddy Mula","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-8299-7059","institution":"JNTUK: Jawaharlal Nehru Technological University Kakinada","correspondingAuthor":true,"prefix":"","firstName":"Samba","middleName":"Siva Reddy","lastName":"Mula","suffix":""},{"id":605625149,"identity":"a1316bbe-02e9-4941-b92b-f6503f1f9222","order_by":1,"name":"Vinay Kumar Kumar Pamula","email":"","orcid":"","institution":"Jawaharlal Nehru Technological University Kakinada","correspondingAuthor":false,"prefix":"","firstName":"Vinay","middleName":"Kumar Kumar","lastName":"Pamula","suffix":""}],"badges":[],"createdAt":"2026-01-25 16:31:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8693763/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8693763/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104705466,"identity":"b7e43733-b50b-4622-9bd4-284d469ff678","added_by":"auto","created_at":"2026-03-16 09:14:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":341687,"visible":true,"origin":"","legend":"","description":"","filename":"ExplainableMLforPathLossmodelingmodified.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8693763/v1_covered_ee356a7d-58f2-4286-8a6c-187737c44080.pdf"},{"id":104705415,"identity":"bdb18d6f-9f7c-46a7-9a86-3e11ed65a736","added_by":"auto","created_at":"2026-03-16 09:14:24","extension":"tex","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":47580,"visible":true,"origin":"","legend":"","description":"","filename":"snarticle.tex","url":"https://assets-eu.researchsquare.com/files/rs-8693763/v1/209721f09eabae0ae2a14888.tex"}],"financialInterests":"","formattedTitle":"Explainable Machine Learning for Path Loss Modeling in mmWave and Sub-THz Communication Systems","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":"Explainable machine learning, path loss modeling, millimeter-wave communications, sub-terahertz systems, wireless propagation, feature attribution, model interpretability, 6G networks","lastPublishedDoi":"10.21203/rs.3.rs-8693763/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8693763/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Machine learning has emerged as a powerful tool for large-scale path loss modeling at millimeter-wave (mmWave) and sub-terahertz (sub-THz) frequencies. 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