  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.
