Time-frequency graph enhancement of communication signals based on generative diffusion model in low signal-to-noise ratio environment

Time-frequency graph enhancement of communication signals based on generative diffusion model in low signal-to-noise ratio environment | 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 Time-frequency graph enhancement of communication signals based on generative diffusion model in low signal-to-noise ratio environment wenqiang Cao, Zhu Rui, Chen Yarong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6746628/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 In low signal-to-noise ratio (LSNR) environments, the processing of communication signals faces significant noise interference. Traditional signal enhancement methods often perform poorly under these conditions, making it difficult to effectively improve signal quality. To address this challenge, this paper proposes a dual-stage signal enhancement method based on an improved DiffBIR model (Diffusion-based Blind Image Restoration Model), aiming to efficiently enhance received signals through time-frequency diagrams, thereby improving signal quality in low SNR environments. The improved DiffBIR model combines the advantages of deep learning and diffusion processes, utilizing Inception and PFA modules to achieve adaptive signal recovery in the time-frequency domain. The Inception module provides a rich feature foundation for the PFA module through a multi-scale feature extraction mechanism, while the PFA module further enhances the accuracy of signal recovery by optimizing the weight distribution of signal regions. Experimental results show that under low SNR conditions, the proposed improved DiffBIR model significantly outperforms traditional signal enhancement methods, particularly in scenarios with very low SNR, where the enhancement effect is especially pronounced. This method offers an innovative solution for enhancing received signals, not only demonstrating high noise suppression capabilities but also better preserving the time-frequency characteristics of the signal. It has broad application prospects, particularly in fields such as communication, radar, and acoustic signal processing. The code and data supporting this research have been stored on GitHub, with the link being https://github.com/18291716943/demo . Physical sciences/Mathematics and computing/Information technology Physical sciences/Mathematics and computing/Computer science DiffBIR model LSNR Signal recovery Time-frequency images Wideband communication signals 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-6746628","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":470269144,"identity":"e7a91b92-6ac2-4641-aad9-38ecd3d4d647","order_by":0,"name":"wenqiang Cao","email":"","orcid":"","institution":"Xijing University","correspondingAuthor":false,"prefix":"","firstName":"wenqiang","middleName":"","lastName":"Cao","suffix":""},{"id":470269145,"identity":"75fde0fb-1161-4667-87a3-250606319c60","order_by":1,"name":"Zhu Rui","email":"","orcid":"","institution":"Xijing University","correspondingAuthor":false,"prefix":"","firstName":"Zhu","middleName":"","lastName":"Rui","suffix":""},{"id":470269146,"identity":"a2d6f7d1-c8dd-4a56-885a-0f8e3c59ea82","order_by":2,"name":"Chen Yarong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYBACfvb2AwYJP9h47I83HyBOi2TPmYSChz18cgxnjiUQp8XgRoLBxwdscsYMN3IMiHTZgYTEDQk8ZomNPWc+3njDYCen20BAB2PDwcMGCRZpic3svZst5zAkG5sdIKCFmbEhzSCB51hiG8/ZbdI8DAcStxHSwsbMYP4jge1/Yo9EzjPitPCwMRgYJLCxGUtI5LARp0WChyfBILGHTc6A55ix5RwDIvxif//5AcMfwKg0YG9+eONNhZ0cQS1oVhIbNUhaSNUxCkbBKBgFIwIAAEnkRJ6lCD5nAAAAAElFTkSuQmCC","orcid":"","institution":"Xijing University","correspondingAuthor":true,"prefix":"","firstName":"Chen","middleName":"","lastName":"Yarong","suffix":""}],"badges":[],"createdAt":"2025-05-26 03:23:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6746628/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6746628/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94985658,"identity":"154e89d1-9412-4aed-bf51-13274e4b2962","added_by":"auto","created_at":"2025-11-03 06:58:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":708778,"visible":true,"origin":"","legend":"","description":"","filename":"Timefrequencygraphenhancementofcommunicationsignalsbasedongenerativediffusionmodelinlowsignaltonoiseratioenvironment.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6746628/v1_covered_503fced8-5991-4c8e-a2c6-cc33e681b565.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Time-frequency graph enhancement of communication signals based on generative diffusion model in low signal-to-noise ratio environment","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":"DiffBIR model, LSNR, Signal recovery, Time-frequency images, Wideband communication signals","lastPublishedDoi":"10.21203/rs.3.rs-6746628/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6746628/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn low signal-to-noise ratio (LSNR) environments, the processing of communication signals faces significant noise interference. Traditional signal enhancement methods often perform poorly under these conditions, making it difficult to effectively improve signal quality. To address this challenge, this paper proposes a dual-stage signal enhancement method based on an improved DiffBIR model (Diffusion-based Blind Image Restoration Model), aiming to efficiently enhance received signals through time-frequency diagrams, thereby improving signal quality in low SNR environments. The improved DiffBIR model combines the advantages of deep learning and diffusion processes, utilizing Inception and PFA modules to achieve adaptive signal recovery in the time-frequency domain. The Inception module provides a rich feature foundation for the PFA module through a multi-scale feature extraction mechanism, while the PFA module further enhances the accuracy of signal recovery by optimizing the weight distribution of signal regions. Experimental results show that under low SNR conditions, the proposed improved DiffBIR model significantly outperforms traditional signal enhancement methods, particularly in scenarios with very low SNR, where the enhancement effect is especially pronounced. This method offers an innovative solution for enhancing received signals, not only demonstrating high noise suppression capabilities but also better preserving the time-frequency characteristics of the signal. It has broad application prospects, particularly in fields such as communication, radar, and acoustic signal processing. 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