Multi-Agent Reinforcement Learning in Adversarial Game Environments: Personalized Anti-Interference Strategies for Heterogeneous UAV Communication

Existing anti-jamming strategies for unmanned aerial vehicle (UAV) networks largely assume homogeneity among UAVs, neglecting the differences in hardware configurations, task requirements, and environmental adaptability. In the face of such heterogeneity, these strategies often fail to effectively counter intelligent jamming and co-channel interference. To address this issue, this paper proposes an intelligent anti-jamming framework designed specifically for the heterogeneous UAV network, allowing each UAV to autonomously adjust its transmission channel and power based on its hardware capabilities and task requirements in a distributed environment. This aims to optimize communication efficiency and reduce energy consumption. We formulate the anti-jamming problem as an adversarial game and confirm the existence of a unique equilibrium point within this model. Moreover, we introduce the novel Personalized Federated Soft Actor-Critic (PFSAC) algorithm, which combines the global model with local models to customize personalized anti-jamming strategies for each UAV, significantly enhancing network performance in complex jamming environments. Simulation results indicate that compared to other methods, our proposed algorithm significantly enhances the anti-jamming capability of heterogeneous UAV networks and performs better than them.