Satellite Assignment Policy Learning for Coexistence in LEO Networks

Abstract

Unlike in terrestrial cellular networks, certain frequency bands for low-earth orbit (LEO) satellite systems have thus far been allocated on a non-exclusive basis. In this context, systems that launch their satellites earlier (referred to as primary systems) are given spectrum access priority over those that launch later, known as secondary systems. For a secondary system to function, it is expected to either coordinate with primary systems or ensure that it does not cause excessive interference to primary ground users. Reliably meeting this interference constraint requires real-time knowledge of the receive beams of primary users, which in turn depends on the primary satellite-to-primary user associations. However, in practice, primary systems have thus far not publicly disclosed their satellite assignment policies; therefore, it becomes essential for secondary systems to develop methods to infer such policies. Assuming there is limited historical data indicating which primary satellites have served which primary users, we propose an end-to-end graph structure learning-based algorithm for learning highest elevation primary satellite assignment policies, that, upon deployment, can directly map the primary satellite coordinates into assignment decisions for the primary users. Simulation results show that our method can outperform the best baseline, achieving approximately a 15% improvement in prediction accuracy.