{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-solution-for-dynamic-spectrum-management-in","title":"A Solution for Dynamic Spectrum Management in Mission-Critical UAV Networks","arxiv_id":"1904.07380","date":"2019-04-16","proceeding":null,"authors":["Alireza Shamsoshoara","Mehrdad Khaledi","Fatemeh Afghah","Abolfazl Razi","Jonathan Ashdown","Kurt Turck"],"abstract":"In this paper, we study the problem of spectrum scarcity in a network of\nunmanned aerial vehicles (UAVs) during mission-critical applications such as\ndisaster monitoring and public safety missions, where the pre-allocated\nspectrum is not sufficient to offer a high data transmission rate for real-time\nvideo-streaming. In such scenarios, the UAV network can lease part of the\nspectrum of a terrestrial licensed network in exchange for providing relaying\nservice. In order to optimize the performance of the UAV network and prolong\nits lifetime, some of the UAVs will function as a relay for the primary network\nwhile the rest of the UAVs carry out their sensing tasks. Here, we propose a\nteam reinforcement learning algorithm performed by the UAV's controller unit to\ndetermine the optimum allocation of sensing and relaying tasks among the UAVs\nas well as their relocation strategy at each time. We analyze the convergence\nof our algorithm and present simulation results to evaluate the system\nthroughput in different scenarios.","url_abs":"http://arxiv.org/abs/1904.07380v1","url_pdf":"http://arxiv.org/pdf/1904.07380v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-solution-for-dynamic-spectrum-management-in","repo_url":"https://github.com/AlirezaShamsoshoara/Fire-Detection-UAV-Aerial-Image-Classification-Segmentation-UnmannedAerialVehicle","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"a-solution-for-dynamic-spectrum-management-in","repo_url":"https://github.com/AlirezaShamsoshoara/Reinforcement_Learning_Team_Q_learnig_MARL_Multi_Agent_UAV_Spectrum_task","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"management","task_name":"Management"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}