Neighboring-Satellite Beam Coordination Framework Based on Satellite Autonomous Control

Due to the uneven distribution of ground communication demands and the multicoverage characteristics of low-Earth orbit (LEO) satellites, a key challenge arises in aligning the limited onboard satellite resources with the heterogeneous ground demands through multisatellite collaboration. Traditional approaches, which rely on centralized beam coverage planning by ground control centers (GCCs), often result in scheduling delays and increased communication overhead, thereby constraining system responsiveness and flexibility. This article proposes a neighboring-satellite (NS) beam coordination framework based on satellite autonomous control, enabling local satellite decision-making for beam coverage and real-time responses to resource allocation demands. Specifically, the framework is divided into two phases: 1) the prediction–approximation phase and 2) the strategy decision phase. In the prediction–approximation phase, satellites predict current communication demands based on historical traffic data from ground cells and approximate the channel state information using easily observable elevation angle data. In the strategy decision phase, an neighboring satellite (NS) collaborative beam hopping algorithm, based on global information sharing and independent policy reinforcement learning, is proposed. This approach enables beam coordination between NSs without the need for intersatellite communication. In a two-satellite neighboring coordination system, the proposed method achieves a throughput improvement of 5.78%–28.54% and a delay reduction of 2.63%–26.56% compared to baseline schemes. Relative to GCC-based approaches, the proposed method significantly reduces control latency and enables faster response. Further scalability and limitations analysis confirms that the proposed framework provides efficient, stable, and robust resource scheduling, with excellent scalability characteristics.