A Novel automatic modulation recognition algorithm toward OFDM signals based on FAFT

A Novel automatic modulation recognition algorithm toward OFDM signals based on FAFT | 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 A Novel automatic modulation recognition algorithm toward OFDM signals based on FAFT YuePeng Li, XiaoGang Tang, Lu Wang, HongJi Xing This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7587017/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Automatic Modulation Recognition (AMR) is crucial and challenging for Cognitive Radio (CR) in 5G and 6G wireless communication scenario demanding with high speed and low latency. Existing Deep Learning (DL) based methods have achieved higher accuracy compared with traditional methods, without model solving the issue of Orthogonal Frequency Division Multiplexing (OFDM) system, which is widely applied in modern wireless communication scenarios. To address this issue, this paper proposes a specially designed Fourier Adaptive Filter Attention (FAFT) framework based on Frequency domain contrastive regularization and adaptive filter. The proposed method achieves to inject the character from time-series and frequency domain of signals specializing in different signal noise ratio (SNR) and different type of communication systems. Experimental results show that, its accuracy can reach 89.1% at SNR of 20 dB under urban channels with 91.8% at SNR of 14 for public dataset RML2016.10a and Multipath effect and Doppler effect for OFDM signals. Compared with state-of-the-art, the proposed method significantly reduces the computational complexity while maintaining recognition accuracy, demonstrating its significant feasibility in practical scenarios. Physical sciences/Engineering Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Supplementary Files dataset.rar Cite Share Download PDF Status: Published Journal Publication published 05 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 10 Nov, 2025 Reviews received at journal 15 Oct, 2025 Reviews received at journal 09 Oct, 2025 Reviewers agreed at journal 08 Oct, 2025 Reviewers agreed at journal 07 Oct, 2025 Reviews received at journal 03 Oct, 2025 Reviewers agreed at journal 22 Sep, 2025 Reviewers invited by journal 22 Sep, 2025 Editor assigned by journal 22 Sep, 2025 Editor invited by journal 22 Sep, 2025 Submission checks completed at journal 16 Sep, 2025 First submitted to journal 16 Sep, 2025 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-7587017","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":524415731,"identity":"bdecfcd3-1859-4a0f-8b29-2fde63abd18f","order_by":0,"name":"YuePeng Li","email":"","orcid":"","institution":"Space Engineering University","correspondingAuthor":false,"prefix":"","firstName":"YuePeng","middleName":"","lastName":"Li","suffix":""},{"id":524415732,"identity":"0a619eac-ff50-4adb-b8f2-7ec1fe77f2f8","order_by":1,"name":"XiaoGang 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