Dynamic and Adaptive GA-based Optimization of Active Reconfigurable Intelligent Surface Assisted MU-MISO Networks

Dynamic and Adaptive GA-based Optimization of Active Reconfigurable Intelligent Surface Assisted MU-MISO Networks | 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 Dynamic and Adaptive GA-based Optimization of Active Reconfigurable Intelligent Surface Assisted MU-MISO Networks Abdalrahman Basheer, Ibrahim Khider This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9066791/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract This paper investigates spectral efficiency enhancement in active reconfigurable intelligent surface (RIS)assisted multi-user multiple-input single-output (MU-MISO) systems. Unlike conventional passive RIS, active RIS can mitigate the multiplicative fading effect by employing reflectiontype amplification, at the cost of increased optimization complexity and strict power constraints. To address the resulting non-convex joint optimization problem, a genetic algorithm (GA)-based framework is proposed for the simultaneous design of base station precoding and active RIS configuration. Two constraint-handling strategies are developed, namely DynamicGA-RIS and Adaptive-GA-RIS, which regulate power constraints through generation-dependent and feasibility-driven penalty mechanisms, respectively. Simulation results demonstrate that the proposed GA-based approaches outperform baseline systems without RIS assistance. In particular, the adaptive strategy achieves higher spectral efficiency, while the dynamic strategy provides faster convergence and improved power regulation. These results confirm the effectiveness of evolutionary optimization techniques for enhancing the performance of active RIS-assisted wireless networks. Active RIS 6G Networks Genetic Algorithm Spectral Efficiency MU-MISO Beamforming Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 19 Apr, 2026 Reviewers invited by journal 19 Apr, 2026 Editor assigned by journal 11 Mar, 2026 First submitted to journal 11 Mar, 2026 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. 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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-9066791","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625903937,"identity":"f451321b-0ace-439d-86cb-b8ef0d742e15","order_by":0,"name":"Abdalrahman Basheer","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYBACNgkGhgM8BjY8/OzNB4B8CRmCWvghWtLkJHuOJYC08BDUIjkDSPAwHDY2uJFjwABmEwIGt7sTD7wpYE5sOJDz+dWNGgseBvbDRzfg1XLn7IaDcwzYEhsbzm6zzjkGdBhPWtoNvFpu5G44zGPAk9jM2LvNOIcNqEWCxwyvFnuIFonENmaeZ8Y5/4jQArXFwJiHjYf5cW4bkVqAfkmQk+BhM2PO7QNSRPhl84c3f/7z2N9//Phzzrc6OX72w8fwakEGoJQAJIlVDgLMH0hRPQpGwSgYBSMHAAAp7U0ZXj9k8QAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0000-4758-6123","institution":"Sudan University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Abdalrahman","middleName":"","lastName":"Basheer","suffix":""},{"id":625903938,"identity":"a289e0be-2ae1-4c99-a66e-dada750b66cf","order_by":1,"name":"Ibrahim Khider","email":"","orcid":"","institution":"Sudan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ibrahim","middleName":"","lastName":"Khider","suffix":""}],"badges":[],"createdAt":"2026-03-08 22:46:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9066791/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9066791/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108007531,"identity":"4203857f-7960-4a47-ab04-1339a32c5742","added_by":"auto","created_at":"2026-04-28 13:00:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":455661,"visible":true,"origin":"","legend":"","description":"","filename":"main1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9066791/v1_covered_a71c8505-dede-4479-8f4c-530ed899f0fa.pdf"}],"financialInterests":"","formattedTitle":"Dynamic and Adaptive GA-based Optimization of Active Reconfigurable Intelligent Surface Assisted MU-MISO Networks","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":"eurasip-journal-on-wireless-communications-and-networking","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jwcn","sideBox":"Learn more about [EURASIP Journal on Wireless Communications and Networking](http://jwcn-eurasipjournals.springeropen.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jwcn/default.aspx","title":"EURASIP Journal on Wireless Communications and Networking","twitterHandle":"@SpringerEng","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Active RIS, 6G Networks, Genetic Algorithm, Spectral Efficiency, MU-MISO, Beamforming","lastPublishedDoi":"10.21203/rs.3.rs-9066791/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9066791/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This paper investigates spectral efficiency enhancement in active reconfigurable intelligent surface (RIS)assisted multi-user multiple-input single-output (MU-MISO) systems. 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