Distributed Communication Interference Resource Scheduling using the Master-Slave Parallel Scheduling Genetic Algorithm

Distributed Communication Interference Resource Scheduling using the Master-Slave Parallel Scheduling Genetic Algorithm | 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 Distributed Communication Interference Resource Scheduling using the Master-Slave Parallel Scheduling Genetic Algorithm Zhenhua Wei, Wenpeng Wu, Jianwei Zhan, Zhaoguang Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4766329/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract With the increasing intelligence and diversification of communication interference in recent years, communication interference resource scheduling has received more attention. However, the existing interference scenario models have been developed mostly for remote high-power interference with a fixed number of jamming devices without considering power constraints. In addition, there have been fewer scenario models for short-range distributed communication interference with a variable number of jamming devices and power constraints. To address these shortcomings, this study designs a distributed communication interference resource scheduling model based distributed communication interference deployment and system operational hours and introduces the stepped logarithmic jamming-to-signal ratio. The proposed model can improve the scheduling ability of the master-slave parallel scheduling genetic algorithm (MSPSGA) in terms of the number of interference devices and the system’s operational time by using four scheduling strategies referring to the searching number, global number, master-slave population power, and fixed-position power. The experimental results show that the MSPSGA can improve the success rate of searching for the minimum number of jamming devices by 40% and prolong the system’s operational time by 128%. In addition, it can reduce the algorithm running time in the scenario with a high-speed countermeasure, the generation time of the jamming scheme, and the average power consumption by 4%, 84%, and 57%, respectively. Further, the proposed resource scheduling model can reduce the search ranges for the number of jamming devices and the system’s operational time by 93% and 79%, respectively. Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Mathematics and computing/Computer science Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 14 Nov, 2024 Reviews received at journal 11 Nov, 2024 Reviews received at journal 09 Nov, 2024 Reviewers agreed at journal 04 Nov, 2024 Reviewers agreed at journal 29 Oct, 2024 Reviewers agreed at journal 29 Oct, 2024 Reviewers agreed at journal 29 Oct, 2024 Reviews received at journal 21 Oct, 2024 Reviewers agreed at journal 16 Oct, 2024 Reviewers invited by journal 08 Oct, 2024 Editor assigned by journal 07 Oct, 2024 Editor invited by journal 29 Jul, 2024 Submission checks completed at journal 29 Jul, 2024 First submitted to journal 19 Jul, 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. 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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-4766329","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":343892044,"identity":"37dbc288-e894-4008-88f0-d785ce2eeb58","order_by":0,"name":"Zhenhua Wei","email":"","orcid":"","institution":"Xi’an Research Institute of High-Tech","correspondingAuthor":false,"prefix":"","firstName":"Zhenhua","middleName":"","lastName":"Wei","suffix":""},{"id":343892045,"identity":"925af37d-c912-471a-b141-6b7c63ec2600","order_by":1,"name":"Wenpeng Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqklEQVRIiWNgGAWjYHACNhCSY2NvPkCaFmM+nmMJpGlJnCeRo0CceoMbucce85QdTm9jyGFg+FGxjRgteenGPOcO57YxnD3A2HPmNmEtZrdzzKRz24BaGPsSmBnbSNCSzsbMY0CalgQ2NmK12N9/lyb951y6YRsPW8JBovwi2XP2mOSMMmt5+fmPDz74UUGEFgYGHhDRDGYeIEY9TEsdkYpHwSgYBaNgRAIAVws617Vj6cwAAAAASUVORK5CYII=","orcid":"","institution":"Xi’an Research Institute of High-Tech","correspondingAuthor":true,"prefix":"","firstName":"Wenpeng","middleName":"","lastName":"Wu","suffix":""},{"id":343892046,"identity":"148556a5-41a4-49ea-969f-686e1ff93fac","order_by":2,"name":"Jianwei Zhan","email":"","orcid":"","institution":"Xi’an Research Institute of High-Tech","correspondingAuthor":false,"prefix":"","firstName":"Jianwei","middleName":"","lastName":"Zhan","suffix":""},{"id":343892047,"identity":"7b9016ee-3a15-4c81-8fc8-c4a008b12d73","order_by":3,"name":"Zhaoguang Zhang","email":"","orcid":"","institution":"Xi’an Research Institute of High-Tech","correspondingAuthor":false,"prefix":"","firstName":"Zhaoguang","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-07-19 05:32:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4766329/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4766329/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-86478-x","type":"published","date":"2025-01-27T15:57:59+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":75351476,"identity":"afd76a92-7172-4e93-bc8f-ae7dabca2565","added_by":"auto","created_at":"2025-02-03 16:11:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":463672,"visible":true,"origin":"","legend":"","description":"","filename":"DistributedCommunicationInterferenceResourceSchedulingusingtheMasterSlaveParallelSchedulingGeneticAlgorithm7.19.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4766329/v1_covered_d96da182-9edc-4808-897a-0322720e0e6e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Distributed Communication Interference Resource Scheduling using the Master-Slave Parallel Scheduling Genetic Algorithm","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4766329/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4766329/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the increasing intelligence and diversification of communication interference in recent years, communication interference resource scheduling has received more attention. However, the existing interference scenario models have been developed mostly for remote high-power interference with a fixed number of jamming devices without considering power constraints. In addition, there have been fewer scenario models for short-range distributed communication interference with a variable number of jamming devices and power constraints. To address these shortcomings, this study designs a distributed communication interference resource scheduling model based distributed communication interference deployment and system operational hours and introduces the stepped logarithmic jamming-to-signal ratio. The proposed model can improve the scheduling ability of the master-slave parallel scheduling genetic algorithm (MSPSGA) in terms of the number of interference devices and the system\u0026rsquo;s operational time by using four scheduling strategies referring to the searching number, global number, master-slave population power, and fixed-position power. The experimental results show that the MSPSGA can improve the success rate of searching for the minimum number of jamming devices by 40% and prolong the system\u0026rsquo;s operational time by 128%. In addition, it can reduce the algorithm running time in the scenario with a high-speed countermeasure, the generation time of the jamming scheme, and the average power consumption by 4%, 84%, and 57%, respectively. 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