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

## TL;DR

This paper proposes a team reinforcement learning approach for dynamic spectrum management in UAV networks, optimizing sensing, relaying, and relocation to enhance performance during critical missions with spectrum scarcity.

## Contribution

It introduces a novel reinforcement learning algorithm for UAVs to dynamically allocate spectrum and tasks, improving network efficiency in mission-critical scenarios.

## Key findings

- The algorithm converges effectively in simulations.
- Optimized spectrum leasing improves data throughput.
- Relocation strategies enhance UAV network longevity.

## Abstract

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.