Efficient Packet Routing for Large-Scale LEO Satellite Networks: A Pareto-Optimal MARL Approach With Queueing Theory

作者：Shuyang Li, G.P. Wu, Qiang Wu, Ran Wang, Hongke Zhang · 发表于：IEEE Internet of Things Journal · 年份：2025 · DOI：10.1109/jiot.2025.3610772 · 被引用次数：4 · 研究领域：Satellite Communication Systems、Interconnection Networks and Systems、Wireless Communication Networks Research

Low Earth orbit (LEO) satellite networks enhance terrestrial connectivity by providing global coverage and low-latency communication. However, their highly dynamic topology, time-varying propagation delays, and constrained bandwidth severely limit the efficiency of conventional centralized routing, underscoring the necessity for adaptive and distributed strategies that can operate effectively under partial observability. Multi-agent reinforcement learning (MARL) offers a promising foundation for such strategies by enabling decentralized, context-aware decision-making based on local information. Nevertheless, existing MARL-based routing approaches often struggle to maintain accurate congestion awareness, reconcile conflicting objectives, and ensure stable convergence in large-scale LEO constellations. To address these challenges, we present POMAP, a packet routing framework that integrates Pareto optimization with multi-agent proximal policy optimization (MAPPO) to achieve efficient and stable trade-offs across multiple key performance metrics. Specifically, we propose a dynamic multi-attribute graph model for LEO satellite networks that simultaneously captures communication delay and energy consumption. Within this framework, each satellite node is represented as a G/G/1/K queue equipped with active queue management and scheduled using weighted priority queueing, thereby enabling precise characterization and control of packet queueing behavior. We formulate the packet routing...