Qikun Zhang

Load-Balanced and Congestion-Aware Routing for LEO Laser Satellite Networks Based on Deep Reinforcement Learning

To address load imbalance in low earth orbit (LEO) laser satellite networks (LSN), this paper proposes a deep reinforcement learning (DRL) based routing algorithm, which combines proximal policy optimization (PPO) and K-shortest path (KSP) strategies to transform the large-scale routing problem into a decision-making process over a small set of paths. Simulation results demonstrate that, compared with traditional Dijkstra and random routing algorithms, the proposed algorithm fully exploits network resources, effectively prevents network bottlenecks, and significantly enhances the network’s service-carrying capacity.