Resource Allocation in Multi-Beam LEO Satellite Systems Based on Beam Hopping and Frequency Reuse

The rapid expansion of low earth orbit (LEO) satellite networks exposes critical limitations in conventional resource allocation schemes, which are unable to simultaneously optimize spectral utilization and adapt to heterogeneous traffic patterns under extreme mobility, necessitating a joint beam-frequency dynamic coordination framework. To address the challenges of multi-beam LEO systems, this paper introduces a hybrid framework that synergizes adaptive beam activation patterns and spectrum reuse optimization, enhanced by a deep reinforcement learning (DRL)-driven coordination mechanism for resource allocation in time. By dynamically adjusting beam activation patterns and frequency allocation, the framework optimizes spatial-temporal resource utilization while mitigating co-channel interference. Angular-constrained multi-criteria clustering achieves dynamic beam-user mapping with low-complexity adaptation for LEO mobility. The DRL-based component further coordinates multi-dimensional parameters, including transmit power and sub-channel assignment, to balance throughput and latency under time-varying channel conditions. Extensive simulations validate the framework’s capability to maintain high spectral efficiency and coverage performance across diverse scenarios, outperforming conventional static allocation methods. The results highlight its adaptability to dynamic traffic patterns and scalability for large-scale deployments, providing a robust foundation for next-generation LEO systems.