Dynamic Beam Scheduling of Multi-NGSO Systems Based on Deep Reinforcement Learning

Non-geostationary orbit (NGSO) satellites, combined with beam hopping (BH) technology, play a vital role in wide-area communications, sensing, and positioning. However, the uneven distribution of ground users and the high mobility of NGSO satellites pose more significant challenges to multi-beam scheduling. This paper proposes a multi-NGSO BH scheduling framework considering the traffic demand of ground users and inter-satellite interference. We decompose the complex optimization problem into two sub-problems. First, we reallocate the satellite service cells to achieve a balanced distribution of satellite service coverage. Then, we propose a multi-NGSO BH algorithm based on soft actor-critic (SAC) to achieve real-time and traffic-driven beam scheduling. Simulation results show that the proposed algorithm outperforms other benchmarks in throughput and service failure rate (SFR).