Quantum adversarial deep RL targets AI-based jamming in LEO satellite networks

[TechRxiv] Silvirianti, Georges Kaddoum, Bassant Selim and Mahdi Chehimi propose in a preprint a quantum adversarial deep reinforcement learning (QADRL) approach to mitigate AI-based jamming attacks and improve quality-of-service (QoS) in low Earth orbit (LEO) satellite networks. According to the paper, QADRL is based on a zero-sum Markov game with two opposing learning networks: one optimizes satellite routing links to avoid jamming and improve QoS, while the other targets the jammer, optimizing unmanned aerial vehicle (UAV) trajectory, jamming nodes and power to maximize jamming success. The paper reports that QADRL outperforms classical adversarial DRL (CADRL) by reducing the jamming success rate by 33.33% and increasing average satellite network QoS by 18.4975%. The findings are from a preprint and have not been peer-reviewed.