  We study the resource sharing problem in a drone-based wireless network. We
consider a distributed control setting under uncertainty (i.e. unavailability
of full information). In particular, the drones cooperate in serving the users
while pooling their spectrum and energy resources in the absence of prior
knowledge about different system characteristics such as the amount of
available power at the other drones. We cast the aforementioned problem as a
Bayesian cooperative game in which the agents (drones) engage in a coalition
formation process, where the goal is to maximize the overall transmission rate
of the network. The drones update their beliefs using a novel technique that
combines the maximum likelihood estimation with Kullback-Leibler divergence. We
propose a decision-making strategy for repeated coalition formation that
converges to a stable coalition structure. We analyze the performance of the
proposed approach by both theoretical analysis and simulations.
