High-capacity and robust information transmission using generalized random structured beams and deep learning-based decoding

High-capacity and robust information transmission using generalized random structured beams and deep learning-based decoding | 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 High-capacity and robust information transmission using generalized random structured beams and deep learning-based decoding Jiayi Yu, Yun Liu, Xinlei Zhu, Jidong Wu, Shuqin Lin, Min Li, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8482044/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract With the rapid growth of data traffic, achieving high-capacity, stable, and secure information transmission has become a critical challenge for free-space optical communication systems. This paper proposes an information transmission scheme based on generalized random structured beams and deep learning-based decoding to address these challenges. By exploiting extended optical coherence engineering, image information is encoded into random modes, enabling a substantial enhancement in channel capacity owing to the theoretically unbounded number of available random modes. At the receiver, a convolutional neural network is employed to decode the transmitted information directly from the intensity distributions of the random modes. Our results demonstrate decoding accuracies exceeding 99% for 256-grayscale image transmission. By incorporating a random pixel-indexing encryption mechanism, the proposed scheme further enhances transmission security. Moreover, reliable information recovery is maintained under strong noise interference, highlighting the robustness of the proposed approach in complex channel environments. We anticipate that integrating optical coherence engineering with deep learning will provide a promising pathway for advancing free-space optical communication systems. Physical sciences/Optics and photonics/Optical techniques Physical sciences/Optics and photonics/Optical physics Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryInformationsubmission.pdf High-capacity and robust information transmission using generalized random structured beams and deep learning-based decoding: Supplementary Information Cite Share Download PDF Status: Under Review Version 1 posted 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-8482044","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":575585708,"identity":"1e6dcb62-0b73-4d42-b282-a4eb676febfe","order_by":0,"name":"Jiayi 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