A Solution for Dynamic Spectrum Management in Mission-Critical UAV Networks
This addresses spectrum management for UAVs in disaster monitoring and public safety, but it is incremental as it builds on existing leasing and relaying concepts.
The paper tackles spectrum scarcity in UAV networks for mission-critical applications by proposing a team reinforcement learning algorithm to optimize task allocation and relocation, resulting in improved system throughput as demonstrated through simulations.
In this paper, we study the problem of spectrum scarcity in a network of unmanned aerial vehicles (UAVs) during mission-critical applications such as disaster monitoring and public safety missions, where the pre-allocated spectrum is not sufficient to offer a high data transmission rate for real-time video-streaming. In such scenarios, the UAV network can lease part of the spectrum of a terrestrial licensed network in exchange for providing relaying service. In order to optimize the performance of the UAV network and prolong its lifetime, some of the UAVs will function as a relay for the primary network while the rest of the UAVs carry out their sensing tasks. Here, we propose a team reinforcement learning algorithm performed by the UAV's controller unit to determine the optimum allocation of sensing and relaying tasks among the UAVs as well as their relocation strategy at each time. We analyze the convergence of our algorithm and present simulation results to evaluate the system throughput in different scenarios.