Bandit based Dynamic Spectrum Anti-jamming Strategy in Software Defined UAV Swarm Network

In this paper, we explore the problem of bandit learning based dynamic spectrum anti-jamming (DSAJ) strategy for enabling reliable information transmission in software defined UAV swarm (SD-UAS) network. We first present UAV swarm network architecture based on software-defined networking (SDN) and describe the multiuser multi-armed bandit (MAB) based anti-jamming channel selection model for SD-UAS network. Then, we propose a collision avoidance (CA) kl-UCB++ channel selection strategy based on the prior spectrum sensing channel information. Simulation results validate that the cumulative regret and the collisions on available channels obtained with the proposed spectrum channel selection strategy outperform the well-known UCB.kl-UCB++ learning algorithm, which can effectively improve the anti-jamming ability of SD-UAV swarm in complicated electromagnetic jamming environment.