A Solution for Dynamic Spectrum Management in Mission-Critical UAV Networks

• Published in: 2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) pp. 1 - 6
• Main Authors: Shamsoshoara, Alireza, Khaledi, Mehrdad, Afghah, Fatemeh, Razi, Abolfazl, Ashdown, Jonathan, Turck, Kurt
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2019
• Subjects: Additives; Current measurement; History; multi-agent systems; Noise measurement; Probability density function; reinforcement learning; Smoothing methods; spectrum sharing; task allocation; Time measurement; UAV networks
• ISSN: 2155-5494
• DOI: 10.1109/SAHCN.2019.8824917

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.