  This paper studies the problem of spectrum shortage in an unmanned aerial
vehicle (UAV) network during critical missions such as wildfire monitoring,
search and rescue, and disaster monitoring. Such applications involve a high
demand for high-throughput data transmissions such as real-time video-, image-,
and voice- streaming where the assigned spectrum to the UAV network may not be
adequate to provide the desired Quality of Service (QoS). In these scenarios,
the aerial network can borrow an additional spectrum from the available
terrestrial networks in the trade of a relaying service for them. We propose a
spectrum sharing model in which the UAVs are grouped into two classes of
relaying UAVs that service the spectrum owner and the sensing UAVs that perform
the disaster relief mission using the obtained spectrum. The operation of the
UAV network is managed by a hierarchical mechanism in which a central
controller assigns the tasks of the UAVs based on their resources and determine
their operation region based on the level of priority of impacted areas and
then the UAVs autonomously fine-tune their position using a model-free
reinforcement learning algorithm to maximize the individual throughput and
prolong their lifetime. We analyze the performance and the convergence for the
proposed method analytically and with extensive simulations in different
scenarios.
