Deep Learning-Empowered Channel Estimation for 6G Vehicle-to-Vehicle Communications

Deep Learning-Empowered Channel Estimation for 6G Vehicle-to-Vehicle Communications | 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 Deep Learning-Empowered Channel Estimation for 6G Vehicle-to-Vehicle Communications Xin Chen, Zhiwei Hou, Yaolin Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4575860/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 Vehicle-to-Vehicle (V2V) communications play a vital role in intelligent transportation. Especially in the 6G environments, the accuracy and efficiency of channel estimation techniques for V2V communication are crucial for realizing reliable autonomous driving and traffic systems. Although the convolutional neural network (CNN) has exhibited notable effectiveness in channel estimation for wireless communication systems, there are still severe open challenges in achieving desirable performance and computation complexity. To fill the gap, a novel deep learning-based channel estimation network (CEN) for multi-scene V2V channel estimation is proposed in this paper. Firstly, a novel bidirectional long short-term memory (Bi-LSTM) framework is introduced for V2V channel estimation. Then, the fully connected neural network (FCNN) network is used for the output dimensionality reduction. Finally, the temporal averaging (TA) processing is designed for eliminating the noise. Simulation results show that the proposed channel estimation scheme is superior to traditional channel estimation algorithms with desirable performance and lower computational load in urban environments. CEN Bi-LSTM V2V fast time-varying channel non-stationary channel 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-4575860","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":317604616,"identity":"71891fe0-f724-4dd3-8632-57ef39dd876f","order_by":0,"name":"Xin Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYDACCRBhwCAH4bGRoMWYVC0MDIkNRGvhn9187DFPwZ30Dbd7DBg+lB0GijQQsOTOsXTDGQbPcmfOOWPAOOPcYaDIAfxaDCRyzCQ+GBzO7ZfIMWDmbTsMFEkgpCX/m0SCweF0NpCWv8RpyWED2ZLAD9LCSIwWiRtpZpIzDA4bzpxzrOBgz7l0HokbBLTwz0h+Js3z57C8we3mjQ9+lFnL8c8goAXJPgaGA0CKh1j1DPA4HQWjYBSMglGAAQDtHj6tBqQcGQAAAABJRU5ErkJggg==","orcid":"","institution":"Xi'an Polytechnic University","correspondingAuthor":true,"prefix":"","firstName":"Xin","middleName":"","lastName":"Chen","suffix":""},{"id":317604617,"identity":"e3bc9ae2-c414-4159-b470-87bf155dd5b1","order_by":1,"name":"Zhiwei Hou","email":"","orcid":"","institution":"Xi'an Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Zhiwei","middleName":"","lastName":"Hou","suffix":""},{"id":317604618,"identity":"0d398276-ddac-475e-9612-3b62bc0d47c9","order_by":2,"name":"Yaolin Zhu","email":"","orcid":"","institution":"Xi'an Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Yaolin","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2024-06-13 11:26:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4575860/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4575860/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61155667,"identity":"c7e39286-4829-4dfc-a888-2c6ab967c229","added_by":"auto","created_at":"2024-07-26 10:17:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":886343,"visible":true,"origin":"","legend":"","description":"","filename":"snarticletemplate.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4575860/v1_covered_a2efcc17-113a-4301-8478-6c8f70f3ff6e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deep Learning-Empowered Channel Estimation for 6G Vehicle-to-Vehicle Communications","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":"CEN, Bi-LSTM, V2V, fast time-varying channel, non-stationary channel","lastPublishedDoi":"10.21203/rs.3.rs-4575860/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4575860/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eVehicle-to-Vehicle (V2V) communications play a vital role in intelligent transportation. 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