Gateway-Assisted Neural Angular Routing for Hierarchical Satellite Networks

초록

The increasing demand for ultralow-latency and high-throughput communication necessitates network architectures capable of ensuring reliable real-time connectivity. To address this requirement, integrated architectures combining terrestrial networks (TNs) and non-TNs (NTNs)-particularly hierarchical multilayer satellite systems composed of geostationary Earth orbit (GEO), medium Earth orbit (MEO), and low Earth orbit (LEO) satellites-have attracted considerable attention. Such hierarchical systems offer global coverage, low latency, and high capacity. However, their highly dynamic topology, fast-moving satellites, and fluctuating link quality pose significant challenges to establishing efficient end-to-end (E2E) routing paths. Traditional routing methods based on static paths or centralized control lack the adaptability required for such environments. To address these challenges, this article introduces a neural hierarchical reinforcement learning (HRL)-based routing algorithm for satellite networks. The framework exploits the layered structure of GEO, MEO, and LEO satellites, enabling gateway-assisted routing and adaptive path selection. Control responsibilities are distributed across orbital layers: the GEO layer allocates hop-count budgets, the MEO layer selects feasible LEO paths, and the LEO layer forwards data packets. When intersatellite links (ISLs) are unavailable, terrestrial gateways provide alternative routes. Routing decisions further account for satellite-to-satellite and satellite-to-ground link quality, with an E2E delay-based reward function capturing transmission, processing, and propagation effects. Simulation results show that the proposed neural hierarchical framework, supported by integrated terrestrial and nonterrestrial networking, achieves faster convergence, greater route stability, and better adaptability than conventional routing algorithms and HRL approaches.

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