Dynamic LEO Satellite Routing Approach Based on Deep Graph Attention and Incremental Evolutionary Reinforcement Learning

Low Earth orbit (LEO) satellite networks are an important component of future 6G. However, due to the unique characteristics of the space environment—such as the complexity in modeling network states and the rapid dynamics of the network topology—existing routing methods often struggle to make appropriate routing decisions in the LEO satellite network context, which significantly limits network transmission performance. In this paper, we propose a dynamic satellite routing method based on deep graph attention and incremental evolution strategy (DGA-IES). Firstly, to address the challenge of accurately perceiving satellite network information, we introduce a topological perception learning model based on deep graph attention. By combining an enhanced message passing process with a self-attention mechanism, this model effectively captures complex features of the LEO network state, including inter-satellite connectivity relationships, as well as the resource states of satellites and links. Secondly, to tackle the problem of inefficient routing re-convergence in rapidly changing topologies, this paper integrates evolution strategies (ES) into deep reinforcement learning (DRL) approaches. We use the global parallel processing capabilities of ES to replace the sequential interactive proximal policy optimization (PPO) strategy in existing DRL. Moreover, we design an incremental evolutionary process based on satellite motion patterns, facilitating efficient routing convergence in highly dynamic satellite environments. Experimental results demonstrate that our DGA-IES approach enhances LEO network load balancing by reducing end-to-end (E2E) network latency by 10.3% 58.1%, decreasing packet loss by 3.8% 20.0%, and improving throughput by 11.1% 57.0% compared with the benchmark approaches.