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. multi-agent systems, reinforcement learning, spectrum sharing, task allocation, UAV networks I. INTRODUCTION Unmanned Aerial Vehicles (UAV) have several advantages such as flexibility, agility, wide field of imaging view and low deployment cost over ground-based infrastructures; hence, are widely used in many applications such as surveillance, disaster relief, wildlife monitoring, and military applications [1]- [6] . Also, UAVs face many security challenges based on the vast area of applications. Many methods such as key sharing, authentication, authorization, and so on have been proposed for these devices in the past few years [7] , [8] . In some of these missions such as disaster monitoring and military applications, the pre-allocated spectrum for UAVs is not sufficient to collect and transmit high-throughput imagery data in certain locations or times, hence an on-demand access to additional spectrum is required. In this paper, we propose a solution for securing extra spectrum for the UAV networks. In this solution, the UAV network leases a portion of time access to the licensed spectrum of a terrestrial network. In order to compensate the spectrum access grant, some of the UAVs provide relaying service for the terrestrial network, while the rest of UAVs perform their regular sensing and transmission tasks using the leased spectrum. The allocation of relaying and sensing tasks among the UAVs as well as selecting their motion strategy is performed by a high-capability drone which serves as controller unit of the entire network. This optimization is performed considering various factors such as the position of the UAVs and ground0based nodes, the quality of communication channels as well as the expected lifetime of the drones. Cooperative spectrum leasing as a property-right model spectrum sharing solution has been studied previously in the context of cognitive radio networks [9]- [14] . The main objective of these works is
In REF , the problem of cooperative spectrum sharing between a UAV network and a terrestrial network was considered, where a fully centralized approach is proposed serving for both task allocation between the sensing and relaying UAVs, and trajectory optimization.
A Solution for Dynamic Spectrum Management in Mission-Critical UAV Networks
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Computer Science