A Hybrid Framework for Blind Channel Estimation and Equalization in Multipath OFDMA Systems

A Hybrid Framework for Blind Channel Estimation and Equalization in Multipath OFDMA 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 Systematic Review A Hybrid Framework for Blind Channel Estimation and Equalization in Multipath OFDMA Systems Said Elkassimi, Bouzid Manaut, Said Safi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7964012/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 This paper addresses the problem of blind channel estimation and equalization in orthogonal frequency-division multiple access (OFDMA) systems over multipath Rayleigh fading channels, modeled according to BRAN-A, BRAN-B, and Proakis B profiles. We investigate and compare three algorithms: the classical Fourth-Order Cumulant method based on higher-order statistics (HOS), a deep learning approach using an autoencoder, and a novel hybrid technique combining cumulant-derived statistical features with autoencoder learning. The Fourth-Order Cumulant method leverages higher-order statistical moments to extract channel information without requiring pilot signals, making it computationally lightweight and analytically interpretable. It performs well in stationary or low-noise conditions with slow channel variations. However, its performance degrades in the presence of high noise, nonlinearities, or rapid fading due to its reliance on rigid statistical assumptions. The autoencoder-based approach employs a neural network to implicitly learn channel effects directly from the magnitude or complex samples of the received signal, without explicit channel modeling. This method offers high adaptability and robustness to nonlinear distortions and time-varying fading, significantly outperforming cumulant-based estimation in complex scenarios. However, it incurs higher computational costs and reduced transparency, as its learned parameters are not directly interpretable. The proposed hybrid method integrates cumulant-based features into the autoencoder’s input space, enabling the network to benefit from both the explicit statistical structure provided by HOS and the data-driven learning capacity of deep neural models. Simulations conducted over a wide range of signal-to-noise ratio (SNR) values and under realistic multipath fading conditions demonstrate that: The cumulant method is optimal for low-noise, slowly varying channels, The autoencoder provides robust performance in nonlinear or rapidly changing environments, and, The hybrid approach consistently achieves the lowest mean square error (MSE) and bit error rate (BER), combining the interpretability and efficiency of cumulants with the adaptability of deep learning. In conclusion, this work highlights the complementarity between statistical and deep learning methods for blind channel estimation and equalization, demonstrating that hybridization can yield a balanced and powerful solution for next-generation wireless systems, such as OFDMA. Estimation Equalization OFDMA Blind Cumulants Hyybrid HOS Bran-A Bran-B Proakis AI SNR MSE BER AE 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. 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-7964012","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":535932087,"identity":"6882a9ca-8eaf-495d-8261-cc6ce37893de","order_by":0,"name":"Said 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Systems","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Estimation, Equalization, OFDMA, Blind, Cumulants, Hyybrid, HOS, Bran-A, Bran-B, Proakis, AI, SNR, MSE, BER, AE","lastPublishedDoi":"10.21203/rs.3.rs-7964012/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7964012/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper addresses the problem of blind channel estimation and equalization in orthogonal frequency-division multiple access (OFDMA) systems over multipath Rayleigh fading channels, modeled according to BRAN-A, BRAN-B, and Proakis B profiles. We investigate and compare three algorithms: the classical Fourth-Order Cumulant method based on higher-order statistics (HOS), a deep learning approach using an autoencoder, and a novel hybrid technique combining cumulant-derived statistical features with autoencoder learning. The Fourth-Order Cumulant method leverages higher-order statistical moments to extract channel information without requiring pilot signals, making it computationally lightweight and analytically interpretable. It performs well in stationary or low-noise conditions with slow channel variations. However, its performance degrades in the presence of high noise, nonlinearities, or rapid fading due to its reliance on rigid statistical assumptions. The autoencoder-based approach employs a neural network to implicitly learn channel effects directly from the magnitude or complex samples of the received signal, without explicit channel modeling. This method offers high adaptability and robustness to nonlinear distortions and time-varying fading, significantly outperforming cumulant-based estimation in complex scenarios. However, it incurs higher computational costs and reduced transparency, as its learned parameters are not directly interpretable. The proposed hybrid method integrates cumulant-based features into the autoencoder\u0026rsquo;s input space, enabling the network to benefit from both the explicit statistical structure provided by HOS and the data-driven learning capacity of deep neural models. Simulations conducted over a wide range of signal-to-noise ratio (SNR) values and under realistic multipath fading conditions demonstrate that: The cumulant method is optimal for low-noise, slowly varying channels, The autoencoder provides robust performance in nonlinear or rapidly changing environments, and, The hybrid approach consistently achieves the lowest mean square error (MSE) and bit error rate (BER), combining the interpretability and efficiency of cumulants with the adaptability of deep learning. In conclusion, this work highlights the complementarity between statistical and deep learning methods for blind channel estimation and equalization, demonstrating that hybridization can yield a balanced and powerful solution for next-generation wireless systems, such as OFDMA.\u003c/p\u003e","manuscriptTitle":"A Hybrid Framework for Blind Channel Estimation and Equalization in Multipath OFDMA Systems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-30 02:58:25","doi":"10.21203/rs.3.rs-7964012/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":"e5b8c783-a34c-4906-954e-6b52052e140a","owner":[],"postedDate":"October 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-09T07:23:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-30 02:58:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7964012","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7964012","identity":"rs-7964012","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}