Waveform Design for Joint Radar-Communication Systems in Urban Micro-cells

Waveform Design for Joint Radar-Communication Systems in Urban Micro-cells | 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 Waveform Design for Joint Radar-Communication Systems in Urban Micro-cells Zacheous Aasa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9254289/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 As the demand for spectral efficiency continues to increase, Joint Radar Communication systems have been identified as a critical component of future 6th Generation wireless networks, particularly within Urban Micro-cells. This environment poses significant challenges, including multi-path fading, considerable shadowing, and the need for high-resolution sensing of dynamic objects such as pedestrians and vehicles. This research aims to optimize an integrated waveform, addressing the trade-off between high data-rate communication and high-resolution radar sensing capabilities. By optimizing the Ambiguity Function for sensing and Spectral Efficiency for communication, this optimized waveform aims to minimize cross-interference between systems, thus achieving maximum utilization of resources. By leveraging a Deep Multi-Agent Reinforcement Learning(MARL) framework and the algorithm of Twin Delayed Deterministic Policy Gradient (TD3), this research aims to dynamically optimize the phase shifts of Reconfigurable Intelligent Surfaces (RIS) to improve waveform beamforming. This study has shown significant findings, proving the optimized waveform’s ability to achieve accurate sensing capabilities within a highly cluttered urban environment, meeting low-latency requirements for micro-cellular communication systems. The simulation data has confirmed a Detection Probability (pd)of 0.95 at a Signal-to-Noise Ratio of 10 dB, as well as a 15% reduction in Root Mean Square Error (RMSE) for radar estimation compared to standard OFDM systems. 6G Wireless Networks Deep Multi-Agent Reinforcement Learning (MARL) Reconfigurable Intelligent Surfaces (RIS) Spectral Efficiency TD3 Algorithm 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. 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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-9254289","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":613947980,"identity":"217c92af-5747-4d0f-9e75-055671e0303a","order_by":0,"name":"Zacheous Aasa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYHACNiR2BRAzMzeQouUMSAsjKVoY28Akfi3y7YefPfhQcdiegf3w4w8/59VG87cDtfyo2IZTC2NPmrnhjDOHExt40swke7cdz51xmLGBsefMbZxamBly2KR52w4nMDAkmDHwbjuW2wDUwszYhlsLG/8bNum/bUCH8T///PHvnGO58wlp4ZEA2sLYBlQmkWMgzdtQk7uBkBYJiWdmkj1n0hPbJN6UScscO5C7EajlID6/yPcnP5P4UWFtz8+fvvnjm5q63HnnDx988KMCtxaEpyDUYTB5gLB6BKgjRfEoGAWjYBSMEAAAvS9W0AOCaloAAAAASUVORK5CYII=","orcid":"","institution":"Ladoke Akintola University of Technology, LAUTECH","correspondingAuthor":true,"prefix":"","firstName":"Zacheous","middleName":"","lastName":"Aasa","suffix":""}],"badges":[],"createdAt":"2026-03-28 16:54:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9254289/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9254289/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106803561,"identity":"26ce9f9c-73ac-43f9-aebb-1e347c2bd717","added_by":"auto","created_at":"2026-04-13 14:58:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":607216,"visible":true,"origin":"","legend":"","description":"","filename":"ZACHWaveformDesignforJointRadarCommunicationSystemsinUrbanMicrocells200005.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9254289/v1_covered_4fdc7890-2598-47d8-97e2-fff1de77e65b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Waveform Design for Joint Radar-Communication Systems in Urban Micro-cells","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":"6G Wireless Networks, Deep Multi-Agent Reinforcement Learning (MARL), Reconfigurable Intelligent Surfaces (RIS), Spectral Efficiency, TD3 Algorithm","lastPublishedDoi":"10.21203/rs.3.rs-9254289/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9254289/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs the demand for spectral efficiency continues to increase, Joint Radar Communication systems have been identified as a critical component of future 6th Generation wireless networks, particularly within Urban Micro-cells. 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