Holistic Interference Management for Wireless Networks in the Era of Artificial Intelligence

Holistic Interference Management for Wireless Networks in the Era of Artificial Intelligence | 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 Holistic Interference Management for Wireless Networks in the Era of Artificial Intelligence Arif Husen, Shafaq Nisar, Muhammad Hasanain Chaudary, Zuhaib Ashfaq Khan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5481165/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Future networks are expected to exhibit intense use of artificial intelligence due to the increasing use of intelligent devices in domestic and industrial life. The intelligent devices will communicate with networks and exchange information about expected performance, available cost packages, and availability of network resources along the destination. Therefore, networks need intelligent techniques to learn the state of various network functions and resources and adjust their configurations in an automated way. Machine learning techniques allow the networks to realize such learning and automate the optimization of the network functions and resources. Several techniques have been discussed in the literature to optimize and manage interference in radio networks. However, the existing approaches generally optimize one or a few aspects in a stand-alone fashion. Recently introduced global learning and deep holistic learning techniques can optimize the network function considering all known aspects. This article proposes a novel holistic learning and optimization technique for interference management in wireless networks. It uses a novel objective functions-based feature engineering process to capture the effects of various parameters and actions related to interference management. Transfer learning reduces computational complexity, and ensemble learning aggregates knowledge from base learners corresponding to each objective function. The experimental network is constructed using the NS3 LENA module, and standard Python libraries are used to implement the base learners and proposed model. It uses several base learners that learn the information from possible interference variables and determine the optimal actions across the cells. The experimental results show that the holistic learning-based approach efficiently manages the interference, improves the system capacity, and reduces the interference caused by user arrivals twofold compared to the state-of-the-art techniques. Holistic learning Global learning Interference management Cross-layer learning Cross-domain learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 02 May, 2025 Reviewers agreed at journal 27 Feb, 2025 Reviewers agreed at journal 26 Feb, 2025 Reviews received at journal 03 Dec, 2024 Reviewers agreed at journal 27 Nov, 2024 Reviewers agreed at journal 24 Nov, 2024 Reviewers invited by journal 22 Nov, 2024 Editor assigned by journal 20 Nov, 2024 Submission checks completed at journal 19 Nov, 2024 First submitted to journal 19 Nov, 2024 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. 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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-5481165","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":383629122,"identity":"411d3506-1f7c-4873-97ef-dd51a63ed5cb","order_by":0,"name":"Arif Husen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYFACxobPIMqAmcHwAZDm4SNCS+NsqBZjA5AWNmKsgWhhYDCTADEIauGffbixuXCPnb05O/O2yq85djJsDMwPH93Ao0XiXGJj84xnyYk7m9nKbstuSwY6jM3YOAefNWcY2x/zHGBOMDjMY3ZbchszUAsPmzQ+LfJnGBubeQ7U24O0FEtuqyesxQCi5TDjBqAWxo/bDhPWYgjRcjxxw2G2YmnGbcd52JgJ+EXuDPtDoJZqe4Pzhzd+/Lmt2p6fvfnhY7zeRwbMPGCSWOUgwPiDFNWjYBSMglEwYgAAp7JFMLnAspMAAAAASUVORK5CYII=","orcid":"","institution":"COMSATS University Islamabad, Pakistan","correspondingAuthor":true,"prefix":"","firstName":"Arif","middleName":"","lastName":"Husen","suffix":""},{"id":383629123,"identity":"11cddb3d-7017-4a03-b5df-9d4cdd8f5acb","order_by":1,"name":"Shafaq Nisar","email":"","orcid":"","institution":"Virtual University of Pakistan","correspondingAuthor":false,"prefix":"","firstName":"Shafaq","middleName":"","lastName":"Nisar","suffix":""},{"id":383629125,"identity":"d07b684f-8fc3-470a-b593-ea63e199bc36","order_by":2,"name":"Muhammad Hasanain Chaudary","email":"","orcid":"","institution":"COMSATS University Islamabad, Pakistan","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Hasanain","lastName":"Chaudary","suffix":""},{"id":383629126,"identity":"dd549813-2126-4ab8-9748-4e05cc75f2ce","order_by":3,"name":"Zuhaib Ashfaq Khan","email":"","orcid":"","institution":"University of Brighton","correspondingAuthor":false,"prefix":"","firstName":"Zuhaib","middleName":"Ashfaq","lastName":"Khan","suffix":""}],"badges":[],"createdAt":"2024-11-19 07:38:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5481165/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5481165/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70428423,"identity":"10b0b62b-960c-4c5e-addb-1993097ddfb1","added_by":"auto","created_at":"2024-12-03 05:38:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1075080,"visible":true,"origin":"","legend":"","description":"","filename":"FinalAritcleSCC.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5481165/v1_covered_373e6a9f-9a61-44e4-994f-8f30998ca848.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Holistic Interference Management for Wireless Networks in the Era of Artificial Intelligence","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cluster-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Cluster Computing](https://www.springer.com/journal/10586)","snPcode":"10586","submissionUrl":"https://submission.nature.com/new-submission/10586/3","title":"Cluster Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Holistic learning, Global learning, Interference management, Cross-layer learning, Cross-domain learning","lastPublishedDoi":"10.21203/rs.3.rs-5481165/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5481165/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFuture networks are expected to exhibit intense use of artificial intelligence due to the increasing use of intelligent devices in domestic and industrial life. 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