Performance Analysis of Deep Learning-Aided OFDM-IM and DCT-OFDM-IM Systems under α-μ and κ-μ Fading Channels with CSI Uncertainty

Performance Analysis of Deep Learning-Aided OFDM-IM and DCT-OFDM-IM Systems under α-μ and κ-μ Fading Channels with CSI Uncertainty | 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 Performance Analysis of Deep Learning-Aided OFDM-IM and DCT-OFDM-IM Systems under α-μ and κ-μ Fading Channels with CSI Uncertainty Anusha Chilupuri, Anuradha S This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8107878/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract This research paper explores the performance of “orthogonal Frequency Division Multiplexing with Index Modulation” (OFDM-IM) and its “Discrete Cosine Transform”-based version (DCT-OFDM-IM) in various wireless channel environments with Channel State Information (CSI) uncertainty, considering both perfect CSI (, Fixed CSI ( and variable CSI. Evaluations are conducted over generalized fading channels, specifically α-μ (α=2, μ=1.5), and κ-μ (k=3, μ=1.5), using a deep learning-based detection (DLD) technique. According to simulation results, DCT-OFDM-IM outperforms conventional OFDM-IM in all fading and CSI condition models, demonstrating superior Bit Error Rate (BER) performance and more robustness. Among the fading models, κ-μ performs more efficiently than α-μ in both perfect and imperfect CSI scenarios. Furthermore, the computational complexity of the novel DLD technique is assessed, demonstrating its real-time usability in spite of its improved detection accuracy. These findings highlight the effectiveness of deep learning-based detection and DCT-OFDM-IM in enhancing communication reliability under a range of challenging and imperfect channel conditions. OFDM-IM DCT-OFDM-IM BER CSI DLD DNN α-μ κ-μ Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 12 Apr, 2026 Reviewers agreed at journal 28 Mar, 2026 Reviewers invited by journal 26 Mar, 2026 Editor assigned by journal 23 Mar, 2026 Submission checks completed at journal 15 Nov, 2025 First submitted to journal 13 Nov, 2025 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-8107878","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":613766901,"identity":"bddc704b-57fe-4200-a65f-69addff02c74","order_by":0,"name":"Anusha Chilupuri","email":"data:image/png;base64,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","orcid":"","institution":"National Institute of Technology Warangal","correspondingAuthor":true,"prefix":"","firstName":"Anusha","middleName":"","lastName":"Chilupuri","suffix":""},{"id":613766902,"identity":"e5fc24e1-04bd-4830-97b0-57780ed9a329","order_by":1,"name":"Anuradha S","email":"","orcid":"","institution":"National Institute of Technology Warangal","correspondingAuthor":false,"prefix":"","firstName":"Anuradha","middleName":"","lastName":"S","suffix":""}],"badges":[],"createdAt":"2025-11-13 16:53:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8107878/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8107878/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105729529,"identity":"826402fd-114e-4966-9961-05818ec4f41c","added_by":"auto","created_at":"2026-03-30 11:17:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1470990,"visible":true,"origin":"","legend":"","description":"","filename":"Springergeneralizedfadingchannelsmodified.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8107878/v1_covered_43b4fe76-0565-4c43-b12a-66f2e7db9500.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":" Performance Analysis of Deep Learning-Aided OFDM-IM and DCT-OFDM-IM Systems under α-μ and κ-μ Fading Channels with CSI Uncertainty","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":"wireless-networks","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wine","sideBox":"Learn more about [Wireless Networks](http://link.springer.com/journal/11276)","snPcode":"11276","submissionUrl":"https://submission.nature.com/new-submission/11276/3","title":"Wireless Networks","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"OFDM-IM, DCT-OFDM-IM, BER, CSI, DLD, DNN, α-μ, κ-μ","lastPublishedDoi":"10.21203/rs.3.rs-8107878/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8107878/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This research paper explores the performance of “orthogonal Frequency Division Multiplexing with Index Modulation” (OFDM-IM) and its “Discrete Cosine Transform”-based version (DCT-OFDM-IM) in various wireless channel environments with Channel State Information (CSI) uncertainty, considering both perfect CSI (, Fixed CSI ( and variable CSI. 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