Joint Optimization Framework For Maximization of Instantaneous Transmission Rate In Signal To Interference Noise Ratio Constrained UAVs-Supported Self-Organized Device-To-Device Network

Joint Optimization Framework For Maximization of Instantaneous Transmission Rate In Signal To Interference Noise Ratio Constrained UAVs-Supported Self-Organized Device-To-Device Network | 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 Joint Optimization Framework For Maximization of Instantaneous Transmission Rate In Signal To Interference Noise Ratio Constrained UAVs-Supported Self-Organized Device-To-Device Network Abhishek Mondal, Ashraf Hossain This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-404001/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 Due to their high maneuverability, flexible deployment, and line of sight (LoS) transmission, unmanned aerial vehicles (UAVs) could be an alternative option for reliable device-to-device (D2D) communication when a direct link is not available between source and destination devices due to obstacles in the signal propagation path. Therefore, in this paper, we have proposed a UAVs-supported self-organized device-to-device (USSD2D) network where multiple UAVs are employed as aerial relays. We have developed a novel optimization framework that maximizes the total instantaneous transmission rate of the network by jointly optimizing the deployed location of UAVs, device association, and UAVs’ channel selection while ensuring that every device should achieve a given signal to interference noise ratio (SINR) constraint. As this joint optimization problem is nonconvex and combinatorial, we adopt reinforcement learning (RL) based solution methodology that effectively decouples it into three individual optimization problems. The formulated problem is transformed into a Markov decision process (MDP) where UAVs learn the system parameters according to the current state and corresponding action aiming to maximize the generated reward under the current policy. Finally, we conceive SARSA, a low complexity iterative algorithm for updating the current policy in the case of randomly deployed device pairs which achieves a good computational complexity-optimality tradeoff. Numerical results validate the analysis and provide various insights on the optimal deployment of UAVs. The proposed methodology improves the total instantaneous transmission rate of the network by 75.37%, 52.08%, and 14.77% respectively as compared with RS-FORD, ES-FIRD, and AOIV schemes. Electrical Engineering Electronic Materials and Devices Unmanned aerial vehicle (UAV) device-to-device (D2D) communication Markov decision process (MDP) reinforcement learning (RL) state-action-reward-state-action (SARSA) signal to interference noise ratio (SINR) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Full Text 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-404001","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":28284167,"identity":"7d8de5e1-1c58-40aa-89ab-5d38013ed653","order_by":0,"name":"Abhishek 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Therefore, in this paper, we have proposed a UAVs-supported self-organized device-to-device (USSD2D) network where multiple UAVs are employed as aerial relays. We have developed a novel optimization framework that maximizes the total instantaneous transmission rate of the network by jointly optimizing the deployed location of UAVs, device association, and UAVs’ channel selection while ensuring that every device should achieve a given signal to interference noise ratio (SINR) constraint. As this joint optimization problem is nonconvex and combinatorial, we adopt reinforcement learning (RL) based solution methodology that effectively decouples it into three individual optimization problems. The formulated problem is transformed into a Markov decision process (MDP) where UAVs learn the system parameters according to the current state and corresponding action aiming to maximize the generated reward under the current policy. Finally, we conceive SARSA, a low complexity iterative algorithm for updating the current policy in the case of randomly deployed device pairs which achieves a good computational complexity-optimality tradeoff. Numerical results validate the analysis and provide various insights on the optimal deployment of UAVs. 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