Team reinforcement learning algorithm performed by the UAV's controller unit toĭetermine the optimum allocation of sensing and relaying tasks among the UAVsĪs well as their relocation strategy at each time. Mobile networks must meet new demands as human communications changes from click and wait/background traffic, to interactive, real-time, haptic communication. While the rest of the UAVs carry out their sensing tasks. With 4G/LTE, high-bandwidth services in mission-critical applications have gotten some traction and will continue to expand as leading-edge public safety, emergency response, and other critical services become commonplace. Its lifetime, some of the UAVs will function as a relay for the primary network The 5G wave will encompass much more than everyday consumer services. In order to optimize the performance of the UAV network and prolong Spectrum of a terrestrial licensed network in exchange for providing relaying In such scenarios, the UAV network can lease part of the Spectrum is not sufficient to offer a high data transmission rate for real-time Unmanned aerial vehicles (UAVs) during mission-critical applications such asĭisaster monitoring and public safety missions, where the pre-allocated
Download a PDF of the paper titled A Solution for Dynamic Spectrum Management in Mission-Critical UAV Networks, by Alireza Shamsoshoara and 5 other authors Download PDF Abstract: In this paper, we study the problem of spectrum scarcity in a network of
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