Automatic Modulation Recognition Method Basedon Multimodal I/Q-FRFT Fusion

Automatic Modulation Recognition Method Basedon Multimodal I/Q-FRFT Fusion | 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 Automatic Modulation Recognition Method Basedon Multimodal I/Q-FRFT Fusion Meng Ning, Fan Zhou, Wei Wang, Yang Wang, Shunchao Fei, Jian Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4946667/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Automatic modulation recognition (AMR) is a key technology in the domain of cognitive radio communications. Accurately identifying the modulation schemes of signals is crucial for enhancing the robustness and reliability of communication systems. The existing methods usually use deep learning technology to intelligently model the single modality such as in-phase quadrature (I/Q). However, single modality has the disadvantages of low recognition rate and limited feature expression ability. In this paper, we propose an innovative automatic modulation recognition method based on multimodal fusion of I and Fractional Fourier Transform (FRFT). Specifically, we first design the entire multimodal approach as an intermediate fusion mode, that is, performing the unimodal representation first and then multimodal fusion later. In the multimodal fusion stage, we introduce a Multimodal Processing Unit (MPU), which realizes the enhancement and aggregation of different modes by learning the complementary features between and within modalities. Furthermore, we employ contrast learning to categorize signals of different modulation into positive and negative samples to enhance the robustness of the multimodal features. In order to verify the effectiveness of the proposed method, we conduct experiments on two public datasets, RML2016.10a and HisarMod2019.1. The experimental results indicate that the multimodal approach consistently outperforms the unimodal methods significantly, achieving the state-of-the-art. Automatic modulation recognition Multimodal fusion Contrast learning Fractional Fourier Transform Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 24 Aug, 2024 Reviewers invited by journal 24 Aug, 2024 Editor assigned by journal 21 Aug, 2024 Submission checks completed at journal 21 Aug, 2024 First submitted to journal 20 Aug, 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. 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-4946667","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":344469733,"identity":"175f86a2-b5d5-4cf6-8ec5-41f907371767","order_by":0,"name":"Meng Ning","email":"","orcid":"","institution":"Shenyang Ligong University","correspondingAuthor":false,"prefix":"","firstName":"Meng","middleName":"","lastName":"Ning","suffix":""},{"id":344469736,"identity":"34f3b9c7-9b64-4f95-9814-adb07d59b460","order_by":1,"name":"Fan 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