Investigation into the Spectral Completion Algorithm Leveraging Dense Connection Autoencoders

Investigation into the Spectral Completion Algorithm Leveraging Dense Connection Autoencoders | 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 Article Investigation into the Spectral Completion Algorithm Leveraging Dense Connection Autoencoders Yepeng Shi, Shunhu Hou, Shengliang Fang, Youchen Fan, You Fu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7932200/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 To address the issue that existing deep learning-based spectrum completion methods focus on Power Spectral Density (PSD) data and are not adapted to the characteristics of Reference Signal Received Power (RSRP) data, making it difficult to balance accuracy and efficiency in sparse RSRP scenarios, a spectrum completion method based on a fully convolutional dense-connected autoencoder is proposed. An RSRP data matrix is constructed as the input, 4 missing scenarios are designed, and a mask matrix is introduced to mark the validity of data. The existing architecture is improved: the encoder is composed of dense connection blocks and transition layers, the decoder is composed of transposed convolution upsampling blocks and dense connection blocks, and an additional bottleneck layer is added between them to preserve feature expression. Masked Mean Squared Error is used as the loss function. Experiments with 3 comparison methods show that compared with the second-best fully convolutional residual autoencoder, the proposed method reduces Root Mean Square Error (RMSE) by 6.41%, increases Structural Similarity Index (SSIM) by 1.77%, and improves Peak Signal-to-Noise Ratio (PSNR) by 1.47 dB. It can support the high-precision generation of spectrum maps from sparse data in scenarios such as 5G/6G spectrum management. Physical sciences/Engineering Physical sciences/Mathematics and computing Spectrum Map Reference Signal Received Power Densely Connected Blocks Mask Mean Square Error Bottleneck Layer 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-7932200","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":535373718,"identity":"35a1fba2-8707-49dd-83ce-d710c3e1553e","order_by":0,"name":"Yepeng 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Autoencoders","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":"Spectrum Map, Reference Signal Received Power, Densely Connected Blocks, Mask Mean Square Error, 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