Interference-Graph-Aided Multi-Agent Reinforcement Learning for Beam Hopping LEO Satellite Networks

Abstract

Beam hopping technology enables LEO satellites to dynamically switch beam coverage positions, providing flexible resource allocation for non-uniformly distributed traffic demands. However, the severe spatial interference coupling among beams makes coordinated scheduling challenging. To address this problem, in this letter, we propose an interference-graph-aided multi-agent reinforcement learning approach that constructs an interference graph to capture beam coupling relationships and injects this structural prior into agent-side representation learning. Each beam agent aggregates information from interference neighbors via graph convolution before estimating local Q-values, which are then decomposed by the standard mixing network. Simulation results demonstrate that the proposed method achieves 34.4\% higher throughput than QMIX with more stable interference management, under non-uniform traffic and co-channel interference settings.