A Hybrid Beam Hopping Scheme for Uneven Traffic and Complex Jamming Environments in LEO Satellites: Integrating Statistical Planning and Reinforcement Learning

The integration of beam hopping (BH) technology into low Earth orbit (LEO) satellite communication systems has emerged as a critical strategy to enhance spectral efficiency and flexibility. However, conventional BH methods often exhibit insufficient robustness when confronted with uneven terrestrial traffic demands and dynamic jamming levels. This article addresses this challenge by proposing a novel hybrid framework that synergizes statistical planning with multiagent reinforcement learning (MARL) to achieve anti-jamming BH for LEO satellites. The proposed framework decomposes the BH scheduling process into two phases: 1) statistical planning and 2) real-time adjustment. A portion of the beams are managed using a low-complexity potential game algorithm for statistical planning, ensuring stability, and efficient resource allocation based on statistical traffic patterns and jamming conditions. The remaining beams are dynamically adjusted using a low information exchange mean-field multiagent proximal policy optimization (MFMAPPO) algorithm, enabling rapid adaptation to instantaneous jamming events and fluctuating user demands. This framework reduces the number of beams considered during training, thereby simplifying the action space. As a result, the system achieves quicker convergence and offers enhanced robustness, especially in environments where some information may be missing. Simulation results demonstrate that the proposed method significantly enhances throughput, improves robustness against jamming, and reduces training time compared to other baselines. The integration of statistical planning and mean-field MARL effectively balances long-term efficiency with real-time adaptability, achieving high-quality communication in LEO satellite coverage areas under high dynamic environments.