Learning-Based Resilient Dynamic Routing for Fault-Tolerant Robust LEO Satellite Networks

초록

Providing seamless global internet connectivity remains a significant challenge due to geographical barriers, economic constraints, and vulnerabilities to catastrophic events, including natural disasters and warfare. Nonterrestrial networks (NTN), particularly satellite communication systems, are crucial for overcoming these limitations, but traditional geostationary Earth orbit (GEO) satellites experience inherent latency issues due to their high-altitude positioning. Low Earth orbit (LEO) satellites have emerged as superior alternatives, significantly reducing communication delays and enhancing network performance. Despite their advantages, LEO satellite networks face significant challenges arising from rapidly changing network topologies driven by high orbital speeds, resulting in frequent shifts in intersatellite link (ISL) availability. Conventional routing algorithms struggle to adapt effectively to these dynamic conditions, resulting in suboptimal routing decisions and increased latency due to congestion-related issues. To address these complexities, this article proposes an adaptive reinforcement learning (RL)-based NTN routing (ARL-NTNR) algorithm designed explicitly for dynamic LEO environments. The proposed ARL-NTNR algorithm autonomously learns and adapts optimal routing policies in real-time by continually interacting with the evolving satellite network. It effectively balances shortest-path routing with real-time queue backlog congestion management, significantly reducing packet delay and minimizing packet loss.

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