Spectrum Sensing to Detect N-Number of Primary Users Using N-Number Secondary Users by Applying Support Vector Machine.

Spectrum Sensing to Detect N-Number of Primary Users Using N-Number Secondary Users by Applying Support Vector Machine. | 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 Spectrum Sensing to Detect N-Number of Primary Users Using N-Number Secondary Users by Applying Support Vector Machine. venkateshkumar Udayamoorthy, Ramakrishnan Srinivasan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-218297/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract In this paper, a cooperative spectrum sensing (CSS) model is proposed to sense n-number of primary users (PUs) using n-number secondary users (SUs) in a sequence by applying support vector machine (SVM) algorithm using three different kernels namely linear, polynomial and radial basis function (RBF) respectively. In this method, fusion centre (FC) instructs all the SUs through control channel, which PU is to be sensed by sending a pre-defined primary user identification code (PUid) and each SU sense the K th PU spectrum information and stored in a database at FC. SU transmits a bit ‘0’ or bit ‘1’ along with PU sensing information to the FC to indicate whether it needs a spectrum band to transmit the data or not. SU add two identification codes along with sensing information to the FC which indicates that from which SU the sensing information received and which PU is sensed by the SU. For simulation 500 data samples are used and the simulation results show an accuracy of 96% and false alarm value of 1.3% in classifying the SU sensing information at FC using RBF kernel. Another method is proposed with multiclass classification by applying SVM algorithm using RBF kernel. The confusion region class is classified with zero false alarm percentage and achieves an accuracy of 99.3% in classifying the SU sensing information at FC. Computer Architecture and Engineering cooperative spectrum sensing classification fusion centre kernel support vector machine Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 Figure 18 Figure 19 Figure 20 Figure 21 Figure 22 Full Text Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the latest manuscript can be downloaded and accessed as a PDF. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 21 Mar, 2021 Editor assigned by journal 08 Feb, 2021 First submitted to journal 06 Feb, 2021 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-218297","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":18020182,"identity":"0ee85288-32e3-4f4b-aaf5-6432a23ccec4","order_by":0,"name":"venkateshkumar Udayamoorthy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIie2PMWuEMBiGFcEpdDYI51+ICJnE+yFdEgK5qXTtINQudjk6e3ToLyjYpXNA8Bapa+A6xKWz003laHQ99OxWaJ7hg+/jfXj5LMtg+KPY2TA9RwhyFw/7g1igIK24VPUNH/elCojCXV6Nl1kFSVZ3Rfq9Cp4z7AO3TV4fK92SxtfTCt+EZY0i9Cm4D8CBvTdUKzW/yaaUpsFQuYiWHql94B0YFlqxs2pGaY9QndD9S0FzH6APhtvugrLfuvAtR8SSzAkLIhIsL7TAfY7h7ikKS8lt1QtGsNQtZOaXq8r5gtvjKgiK217QU7LG7aZTfRpPKmfQMUmWxgfWvwkbDAbD/+AHafNqNsUYgCEAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-4950-3069","institution":"Sri Krishna College of Technology","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"venkateshkumar","middleName":"","lastName":"Udayamoorthy","suffix":""},{"id":18020183,"identity":"f0fe8d85-f5ac-4382-a800-f8218aa265af","order_by":1,"name":"Ramakrishnan Srinivasan","email":"","orcid":"","institution":"Dr Mahalingam College of Engineering and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ramakrishnan","middleName":"","lastName":"Srinivasan","suffix":""}],"badges":[],"createdAt":"2021-02-07 07:14:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-218297/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-218297/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":7304256,"identity":"70381f7c-bdca-4e59-ad00-485ef666ab0f","added_by":"auto","created_at":"2021-03-24 10:47:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":174053,"visible":true,"origin":"","legend":"Spectrum utilisation chart report by Federal Communication Commission","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-218297/v1/c5f0c57c50ef1f28eab0c4bb.png"},{"id":7304711,"identity":"970f8f5a-0f13-406e-b9e8-1abafde81acc","added_by":"auto","created_at":"2021-03-24 10:50:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":42246,"visible":true,"origin":"","legend":"Conventional cooperative sensing model.","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-218297/v1/248800084083fbe1a1d2bd99.png"},{"id":7303930,"identity":"7456ddbd-55fb-48c3-98f8-28768f29360c","added_by":"auto","created_at":"2021-03-24 10:44:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":17875,"visible":true,"origin":"","legend":"Conventional energy detection technique","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-218297/v1/f0523a56f810dc39e237b249.png"},{"id":7303940,"identity":"2dc8556f-8712-4d38-9f68-de7443f9174b","added_by":"auto","created_at":"2021-03-24 10:44:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":57560,"visible":true,"origin":"","legend":"Proposed system model using SVM at fusion centre","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-218297/v1/94d9f278e1c3064f55e530ba.png"},{"id":7303941,"identity":"b1405569-bcae-49d8-8e19-d4527f635432","added_by":"auto","created_at":"2021-03-24 10:44:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":33858,"visible":true,"origin":"","legend":"Proposed system block diagram of spectrum sensing using SVM","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-218297/v1/b041acc0ac0688cd825cd50c.png"},{"id":7304259,"identity":"87486231-c8aa-4003-b881-7827be9474ed","added_by":"auto","created_at":"2021-03-24 10:47:13","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":22921,"visible":true,"origin":"","legend":"Sensing and transmitted signal duration frame slot","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-218297/v1/d30fb9e24f50268ed409c8f5.png"},{"id":7304710,"identity":"234488a8-e1af-4c21-8805-f63f64117dfb","added_by":"auto","created_at":"2021-03-24 10:50:13","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":42295,"visible":true,"origin":"","legend":"Proposed system model using multi-class SVM","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-218297/v1/5d420bf76f74fd9d8a47bcf6.png"},{"id":7303936,"identity":"cc0977aa-17fd-4780-8184-c2c26df28227","added_by":"auto","created_at":"2021-03-24 10:44:13","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":40091,"visible":true,"origin":"","legend":"Proposed system block diagram using multi-class SVM","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-218297/v1/91d70aacd95c0ef28b1d9cad.png"},{"id":7303951,"identity":"aca3214e-f231-46a6-967b-60d8b71b3ec6","added_by":"auto","created_at":"2021-03-24 10:44:16","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":44646,"visible":true,"origin":"","legend":"Supervised ML flow diagram","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-218297/v1/0d0049b0f330b55848eb8f39.png"},{"id":7304255,"identity":"53457f01-2593-4f67-94db-34dbbb5fa559","added_by":"auto","created_at":"2021-03-24 10:47:12","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":58137,"visible":true,"origin":"","legend":"SVM classification for 50 SUs as test samples using linear kernel","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-218297/v1/7a52ca8284a9d336b5b43191.png"},{"id":7304254,"identity":"2b049bd2-f247-4857-8ebb-ba92b1cf6b73","added_by":"auto","created_at":"2021-03-24 10:47:12","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":62429,"visible":true,"origin":"","legend":"SVM classification for 100 SUs as test samples using linear kernel","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-218297/v1/2aa7826c12373e8887a740b9.png"},{"id":7304261,"identity":"653831bf-c9ef-444a-9d05-f7fffbe0b285","added_by":"auto","created_at":"2021-03-24 10:47:15","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":70657,"visible":true,"origin":"","legend":"SVM classification for 125 SUs as test samples using linear kernel","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-218297/v1/902c801620ba745c174e3253.png"},{"id":7304260,"identity":"fa29d86e-6750-4f95-afd6-1e5bd4586426","added_by":"auto","created_at":"2021-03-24 10:47:15","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":53795,"visible":true,"origin":"","legend":"SVM classification for 50 SUs as test samples using polynomial kernel","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-218297/v1/f9b3ff88adcf68fff8074d20.png"},{"id":7304264,"identity":"c44274af-3b9c-4d99-b1a0-e9d8b10b1fc9","added_by":"auto","created_at":"2021-03-24 10:47:16","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":63338,"visible":true,"origin":"","legend":"SVM classification for 50 SUs as test samples using polynomial kernel","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-218297/v1/31f348b20522963147974502.png"},{"id":7303954,"identity":"964d5bf0-8bd9-4afd-98c8-4fe6a4bfd91b","added_by":"auto","created_at":"2021-03-24 10:44:17","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":73443,"visible":true,"origin":"","legend":"SVM classification for 125 SUs as test samples using polynomial kernel","description":"","filename":"15.png","url":"https://assets-eu.researchsquare.com/files/rs-218297/v1/db849caf1608c536c3bfd674.png"},{"id":7304265,"identity":"46b0c527-4b0a-4588-bbe1-45f078aeac06","added_by":"auto","created_at":"2021-03-24 10:47:17","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":59150,"visible":true,"origin":"","legend":"SVM classification for 50 SUs as test samples using RBF kernel","description":"","filename":"16.png","url":"https://assets-eu.researchsquare.com/files/rs-218297/v1/03fffde661cced4e92ef477b.png"},{"id":7303942,"identity":"276bdec6-6bde-4306-900d-56751b2ab5e9","added_by":"auto","created_at":"2021-03-24 10:44:14","extension":"png","order_by":17,"title":"Figure 17","display":"","copyAsset":false,"role":"figure","size":67723,"visible":true,"origin":"","legend":"SVM classification for 100 SUs as test samples using RBF kernel","description":"","filename":"17.png","url":"https://assets-eu.researchsquare.com/files/rs-218297/v1/49b4acfdc071e10dd089f39f.png"},{"id":7303939,"identity":"3407eca9-6983-49ba-a652-77320c11acfa","added_by":"auto","created_at":"2021-03-24 10:44:13","extension":"png","order_by":18,"title":"Figure 18","display":"","copyAsset":false,"role":"figure","size":69283,"visible":true,"origin":"","legend":"SVM classification for 125 SUs as test samples using RBF kernel","description":"","filename":"18.png","url":"https://assets-eu.researchsquare.com/files/rs-218297/v1/89bcc51a9d391f8f67cd9230.png"},{"id":7303944,"identity":"588b7459-54da-488a-bea9-206985f0abf8","added_by":"auto","created_at":"2021-03-24 10:44:15","extension":"png","order_by":19,"title":"Figure 19","display":"","copyAsset":false,"role":"figure","size":222923,"visible":true,"origin":"","legend":"Percentage of accuracy of three different kernels to classify n-number of SUs by the fusion centre","description":"","filename":"19.png","url":"https://assets-eu.researchsquare.com/files/rs-218297/v1/e027066736d5ae3a173c9c4b.png"},{"id":7303946,"identity":"c3dcf615-ae36-404d-92db-d3b658e6726d","added_by":"auto","created_at":"2021-03-24 10:44:15","extension":"png","order_by":20,"title":"Figure 20","display":"","copyAsset":false,"role":"figure","size":247310,"visible":true,"origin":"","legend":"Percentage of false alarm of three different kernels to classify n-number of SUs sensing information by the fusion centre","description":"","filename":"20.png","url":"https://assets-eu.researchsquare.com/files/rs-218297/v1/11afd73e2bfa995d6ee6a569.png"},{"id":7304257,"identity":"485929ef-3d70-46c2-84d5-d606281bc414","added_by":"auto","created_at":"2021-03-24 10:47:13","extension":"png","order_by":21,"title":"Figure 21","display":"","copyAsset":false,"role":"figure","size":216896,"visible":true,"origin":"","legend":"Percentage of accuracy for three different classes of different kernels to classify the SUs by the fusion centre.","description":"","filename":"21.png","url":"https://assets-eu.researchsquare.com/files/rs-218297/v1/068dc88478fc781b86a337e4.png"},{"id":7304266,"identity":"9f6def2c-c05e-4053-bddc-c457006b20c9","added_by":"auto","created_at":"2021-03-24 10:47:17","extension":"png","order_by":22,"title":"Figure 22","display":"","copyAsset":false,"role":"figure","size":190847,"visible":true,"origin":"","legend":"Percentage of false alarm for three different classes to classify the SUs by using RBF kernel","description":"","filename":"22.png","url":"https://assets-eu.researchsquare.com/files/rs-218297/v1/b897bf5973e13ce6abcf32ab.png"},{"id":13682198,"identity":"1a41691a-1392-4fa2-81d2-df53742c1824","added_by":"auto","created_at":"2021-09-17 11:55:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2101560,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-218297/v1/5785a951-cec0-4fb8-b8ff-3cc04ff0112d.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eSpectrum Sensing to Detect N-Number of Primary Users Using N-Number Secondary Users by Applying Support Vector Machine.\u003c/p\u003e","fulltext":[{"header":"Full Text","content":"Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the latest manuscript can be downloaded and \u003ca href='/article/rs-218297/latest.pdf' target='_blank'\u003e accessed as a PDF.\u003c/a\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"wireless-personal-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wire","sideBox":"Learn more about [Wireless Personal Communications](https://www.springer.com/journal/11277)","snPcode":"11277","submissionUrl":"https://submission.nature.com/new-submission/11277/3","title":"Wireless Personal Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"cooperative spectrum sensing, classification, fusion centre, kernel, support vector machine","lastPublishedDoi":"10.21203/rs.3.rs-218297/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-218297/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this paper, a cooperative spectrum sensing (CSS) model is proposed to sense n-number of primary users (PUs) using n-number secondary users (SUs) in a sequence by applying support vector machine (SVM) algorithm using three different kernels namely linear, polynomial and radial basis function (RBF) respectively. In this method, fusion centre (FC) instructs all the SUs through control channel, which PU is to be sensed by sending a pre-defined primary user identification code (PUid) and each SU sense the K\u003csup\u003eth\u003c/sup\u003e PU spectrum information and stored in a database at FC. SU transmits a bit ‘0’ or bit ‘1’ along with PU sensing information to the FC to indicate whether it needs a spectrum band to transmit the data or not. SU add two identification codes along with sensing information to the FC which indicates that from which SU the sensing information received and which PU is sensed by the SU. For simulation 500 data samples are used and the simulation results show an accuracy of 96% and false alarm value of 1.3% in classifying the SU sensing information at FC using RBF kernel. Another method is proposed with multiclass classification by applying SVM algorithm using RBF kernel. The confusion region class is classified with zero false alarm percentage and achieves an accuracy of 99.3% in classifying the SU sensing information at FC.\u003c/p\u003e","manuscriptTitle":"Spectrum Sensing to Detect N-Number of Primary Users Using N-Number Secondary Users by Applying Support Vector Machine.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-03-24 10:44:09","doi":"10.21203/rs.3.rs-218297/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2021-03-22T00:00:00+00:00","index":0,"fulltext":""},{"type":"editorAssigned","content":"","date":"2021-02-09T00:00:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Wireless Personal Communications","date":"2021-02-06T23:47:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"wireless-personal-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wire","sideBox":"Learn more about [Wireless Personal Communications](https://www.springer.com/journal/11277)","snPcode":"11277","submissionUrl":"https://submission.nature.com/new-submission/11277/3","title":"Wireless Personal Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b1483e70-c839-458f-bb91-ce30eeb9772a","owner":[],"postedDate":"March 24th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":3182940,"name":"Computer Architecture and Engineering"}],"tags":[],"updatedAt":"2023-08-08T23:53:05+00:00","versionOfRecord":[],"versionCreatedAt":"2021-03-24 10:44:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-218297","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-218297","identity":"rs-218297","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}