AMC-Transformer: Enhanced Automatic Modulation Classification based on Attention Model | 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 AMC-Transformer: Enhanced Automatic Modulation Classification based on Attention Model yuewen xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4751132/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 5 You are reading this latest preprint version Abstract Wireless signal recognition plays a critical role in both military and civilian domains. Developing high-precision Automatic Modulation Classification (AMC) algorithms is essential for effective spectrum management in today's complex wireless communication environment. Deep learning-based AMC techniques have shown promising results in extracting effective classification rules from complex modulation signals. In this work, we propose a novel transformer-based model for AMC that leverages an attention mechanism to enhance recognition accuracy. Our model divides In-phase and Quadrature (IQ) signal sequences into fixed-length patches and utilizes multiple transformer layers to capture complex temporal and spatial feature correlations. Extensive experiments have been conducted to demonstrate that our proposed model outperforms the typical CNN and ResNet models, achieving a 25% and 15% improvement in recognition accuracy at a signal-to-noise ratio of 10 dB, respectively. The proposed attention-based transformer model provides a powerful and efficient approach for AMC in complex wireless communication environments. AMC Deep Learning Transformer Attention Mechanism IQ Signal Full Text Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Minor revision 06 Aug, 2025 Reviewers agreed at journal 11 Apr, 2025 Reviewers invited by journal 10 Apr, 2025 Editor assigned by journal 28 Jul, 2024 First submitted to journal 27 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. 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. 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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-4751132","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":441019755,"identity":"74aca28b-c2a2-434b-bc3f-d6177a507157","order_by":0,"name":"yuewen 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