A Multi-Agent Deep Reinforcement Learning Anti-Jamming Spectrum-Access Method in LEO Satellites

Low-Earth-orbit (LEO) satellite networks face significant vulnerabilities to malicious jamming and co-channel interference, compounded by dynamic topologies, resource constraints, and complex electromagnetic environments. Traditional anti-jamming approaches lack adaptability, centralized intelligent methods incur high overhead, and distributed intelligent methods fail to achieve global optimization. To address these limitations, this paper proposed a value decomposition network (VDN)-based multi-agent deep reinforcement learning (DRL) anti-jamming spectrum access approach with a centralized training and distributed execution architecture. Following offline centralized ground-based training, the model was deployed distributedly on satellites for real-time spectrum-access decision-making. The simulation results demonstrate that the proposed method effectively balances training costs with anti-jamming performance. The method achieved near-optimal user satisfaction (approximately 97%) with minimal link overhead, confirming its effectiveness for resource-constrained LEO satellite networks.