Modulation Signal Recognition Based on Endpoint Detection and Cepstral Parameters

Modulation Signal Recognition Based on Endpoint Detection and Cepstral Parameters | 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 Modulation Signal Recognition Based on Endpoint Detection and Cepstral Parameters Li Xiuquan, Wang Zhen, Jin Yeyin, Chen Jing, Li Zhenfei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4251765/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Aug, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Digital signal modulation recognition technology serves as the foundation and basis for signal demodulation, playing a crucial role in communication signal reconnaissance and holding significant research significance. This paper conducts research on digital signal modulation recognition technology from the perspectives of signal preprocessing and feature extraction. Seven modulation signals, namely 2ASK, 4ASK, 2FSK, 4FSK, 2PSK, 4PSK, and OFDM, are selected as recognition targets. The paper compares the effects of four different endpoint detection algorithms on modulation signal recognition. The results indicate that, for these seven modulation signals, the short-time energy entropy ratio algorithm performs the best, achieving a correct endpoint detection rate of over 93% in a Gaussian channel with a signal-to-noise(SNR) ratio of 0dB. Based on this, three different denoising algorithms are introduced to further enhance the performance of the short-time energy entropy ratio algorithm. The results show that the wavelet denoising algorithm achieves the greatest improvement in the performance of the short-time energy entropy ratio algorithm, with a short processing time. In a Gaussian channel with a SNR ratio greater than − 10dB, the endpoint detection accuracy of this algorithm can be maintained at over 95%. Finally, for the accurate identification and differentiation of 2FSK and 4FSK, this paper optimized the relevant algorithm in the cyclic spectrum. The kurtosis coefficient value Kur of the cyclic spectrum parameter matrix at the cyclic frequency \(\alpha =0\) is utilized to distinguish between these two signals. The results show that, at a SNR ratio of 4dB, the modulation recognition algorithm proposed in this paper can effectively distinguish between these two signals, achieving a recognition accuracy of over 99%. Communication technology Modulation recognition Endpoint detection Cyclic spectrum parameters Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 19 Aug, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 29 May, 2024 Reviews received at journal 14 May, 2024 Reviews received at journal 13 May, 2024 Reviewers agreed at journal 10 May, 2024 Reviewers agreed at journal 07 May, 2024 Reviewers invited by journal 07 May, 2024 Editor assigned by journal 24 Apr, 2024 Editor invited by journal 23 Apr, 2024 Submission checks completed at journal 23 Apr, 2024 First submitted to journal 11 Apr, 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-4251765","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":295396600,"identity":"3bb8496f-bd08-4b66-a144-00cb5637ba90","order_by":0,"name":"Li Xiuquan","email":"","orcid":"","institution":"Hangzhou Institute of Computer External Equipment","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Xiuquan","suffix":""},{"id":295396603,"identity":"98445507-b2f2-47d3-bc94-f78302b06297","order_by":1,"name":"Wang Zhen","email":"","orcid":"","institution":"Hangzhou Dianzi University","correspondingAuthor":false,"prefix":"","firstName":"Wang","middleName":"","lastName":"Zhen","suffix":""},{"id":295396606,"identity":"a8481d73-d063-411a-9dd4-38d0fa2f32cf","order_by":2,"name":"Jin Yeyin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYBACfvbmAwckDGp4+Nl7GCSAAowNhLRI9hxLfGBRcUxOsucMkVoMbuQYG1ScYTYGMojUwnAgwUziZhtbYsPNtwdv8zDYyG44wPzsAT4djA0H0iRntskkNs7OS7bmYUgz3nCAzdwAnxZmxoZj0pJAW5qlc8ykeRgOJ244wMMmgU8LGzNjm/TfNubENskzIC3/CWvhYWNmNpAAep9Hggek5QBhLRI8bIwPJICBLMGTY2w5xyDZeOZhNjO8Wuzvv/8Ajkr742cMb7ypsJPtO978DK8WNAAKKmYS1I+CUTAKRsEowA4AtexH0uraFd8AAAAASUVORK5CYII=","orcid":"","institution":"Hangzhou Dianzi University","correspondingAuthor":true,"prefix":"","firstName":"Jin","middleName":"","lastName":"Yeyin","suffix":""},{"id":295396609,"identity":"19aa8b0d-ed11-4ef7-8205-4b75b4bd87a1","order_by":3,"name":"Chen Jing","email":"","orcid":"","institution":"Hangzhou Dianzi University","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Jing","suffix":""},{"id":295396611,"identity":"92d1869f-5282-47aa-b5ca-c126bfe0e3fd","order_by":4,"name":"Li Zhenfei","email":"","orcid":"","institution":"Hangzhou Dianzi University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Zhenfei","suffix":""}],"badges":[],"createdAt":"2024-04-11 10:16:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4251765/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4251765/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-69934-y","type":"published","date":"2024-08-19T15:57:29+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":63300203,"identity":"b21d02fc-8ceb-4a0f-a99a-b6eb0c38c138","added_by":"auto","created_at":"2024-08-26 16:12:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":632171,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4251765/v1_covered_4135a433-51d4-47e7-b050-1007406769df.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Modulation Signal Recognition Based on Endpoint Detection and Cepstral Parameters","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":"Communication technology, Modulation recognition, Endpoint detection, Cyclic spectrum parameters","lastPublishedDoi":"10.21203/rs.3.rs-4251765/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4251765/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDigital signal modulation recognition technology serves as the foundation and basis for signal demodulation, playing a crucial role in communication signal reconnaissance and holding significant research significance. This paper conducts research on digital signal modulation recognition technology from the perspectives of signal preprocessing and feature extraction. Seven modulation signals, namely 2ASK, 4ASK, 2FSK, 4FSK, 2PSK, 4PSK, and OFDM, are selected as recognition targets. The paper compares the effects of four different endpoint detection algorithms on modulation signal recognition. The results indicate that, for these seven modulation signals, the short-time energy entropy ratio algorithm performs the best, achieving a correct endpoint detection rate of over 93% in a Gaussian channel with a signal-to-noise(SNR) ratio of 0dB. Based on this, three different denoising algorithms are introduced to further enhance the performance of the short-time energy entropy ratio algorithm. The results show that the wavelet denoising algorithm achieves the greatest improvement in the performance of the short-time energy entropy ratio algorithm, with a short processing time. In a Gaussian channel with a SNR ratio greater than \u0026minus;\u0026thinsp;10dB, the endpoint detection accuracy of this algorithm can be maintained at over 95%. Finally, for the accurate identification and differentiation of 2FSK and 4FSK, this paper optimized the relevant algorithm in the cyclic spectrum. The kurtosis coefficient value \u003cem\u003eKur\u003c/em\u003e of the cyclic spectrum parameter matrix at the cyclic frequency \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\alpha =0\\)\u003c/span\u003e\u003c/span\u003e is utilized to distinguish between these two signals. The results show that, at a SNR ratio of 4dB, the modulation recognition algorithm proposed in this paper can effectively distinguish between these two signals, achieving a recognition accuracy of over 99%.\u003c/p\u003e","manuscriptTitle":"Modulation Signal Recognition Based on Endpoint Detection and Cepstral Parameters","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-26 20:34:36","doi":"10.21203/rs.3.rs-4251765/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-30T03:30:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-14T20:14:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-13T11:37:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"281298520065414241750740427774790621313","date":"2024-05-10T14:15:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62863688429007903722639767912287642087","date":"2024-05-07T15:17:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-07T13:27:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-24T12:59:18+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-04-23T14:36:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-23T05:36:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-04-11T10:15:27+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"51ccda26-3a0a-487f-9f6c-73e465c68d9d","owner":[],"postedDate":"April 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-26T16:02:07+00:00","versionOfRecord":{"articleIdentity":"rs-4251765","link":"https://doi.org/10.1038/s41598-024-69934-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-08-19 15:57:29","publishedOnDateReadable":"August 19th, 2024"},"versionCreatedAt":"2024-04-26 20:34:36","video":"","vorDoi":"10.1038/s41598-024-69934-y","vorDoiUrl":"https://doi.org/10.1038/s41598-024-69934-y","workflowStages":[]},"version":"v1","identity":"rs-4251765","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4251765","identity":"rs-4251765","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}