Deep Neural Networks-based Intelligent UAV Trajectory Planning in 5G and Beyond Non-Terrestrial Communication Networks

Deep Neural Networks-based Intelligent UAV Trajectory Planning in 5G and Beyond Non-Terrestrial Communication Networks | 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 Deep Neural Networks-based Intelligent UAV Trajectory Planning in 5G and Beyond Non-Terrestrial Communication Networks Sanaullah Manzoor, Muhammad Zeeshan Shakir, Mazen Hasna, Khalid Qaraqe, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7736532/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In recent times, unmanned aerial vehicles (UAVs) have been used in many real-world applications such as trans-portations and delivery, agriculture and forestry, infrastructure inspection and surveying, surveillance, and counter-terrorism, wireless communications, and emergency and disaster management. For their role in disaster prevention and control, UAV operations as flying base stations (FBSs) have become pivotal in enhancing network coverage and quality of service. Therefore, autonomous FBS deployment has gained significant research interest due to UAV control and manoeuvring in dynamic and challenging environments. Further, these deployments hinge on efficient trajectory planning along with obstacle avoidance to reach their destination without depleting limited battery resources. To tackle this challenge, wireless channel characteristics such as channel state information (CSI) can help UAVs avoid obstacles and reach their destination point. In this work, we propose a novel deep learning-based FBS trajectory prediction framework that uses CSI information to dynamically learn environmental characteristics while avoiding obstacles and minimising flight time. By integrating a recurrent convolutional neural network (RCNN) architecture, our framework achieves a remarkable 98.34% prediction accuracy in identifying optimal trajectories, significantly outperforming deep reinforcement learning (DRL)-based 85.42% and traditional machine learning (ML)-based schemes 93.25%. Extensive simulations demonstrate that our approach reduces the flight time of the FBS by nearly 44 seconds (47.4%) compared to existing methods under obstacle-based conditions. Our approach demonstrates energy-efficient FBS trajectory planning and underscores its significance for applications in wireless communications, emergency response, and disaster management. Physical sciences/Engineering Physical sciences/Mathematics and computing Earth and environmental sciences/Natural hazards Non-Terrestrial Network UAV Trajectory Deep Neural Networks Wireless Network Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted 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. 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-7736532","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":529037985,"identity":"55477618-97ed-4831-85cc-79b8c904491e","order_by":0,"name":"Sanaullah Manzoor","email":"","orcid":"","institution":"Glasgow Caledonian University","correspondingAuthor":false,"prefix":"","firstName":"Sanaullah","middleName":"","lastName":"Manzoor","suffix":""},{"id":529037986,"identity":"7fb8c809-ae33-4010-b50a-047b04c07051","order_by":1,"name":"Muhammad Zeeshan 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