Asynchronous Risk-Aware Multi-Agent Packet Routing for Ultra-Dense LEO Satellite Networks

2025-10-31

AI Summary

Addressing routing challenges in ultra-dense low-Earth-orbit (LEO) satellite networks—characterized by dynamic topologies, asynchronous communications, and conflicting QoS objectives—this paper proposes a decentralized asynchronous multi-agent routing framework. The method innovatively integrates an event-driven asynchronous decision-making mechanism with risk-aware optimization based on the primal-dual method, enabling agents to learn the distribution of QoS costs and explicitly constrain tail risk to jointly optimize latency, link load, and other objectives. Unlike conventional approaches relying on global synchronization and ignoring risk, our framework operates without centralized coordination or synchronous updates. In simulations over a 1584-satellite constellation, it reduces queueing delay by over 70% compared to a risk-agnostic baseline and decreases end-to-end latency by nearly 12 ms under high-load conditions, significantly enhancing robustness and adaptability to network dynamics.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Multiagent Systems: Mechanism Design
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The rise of ultra-dense LEO constellations creates a complex and asynchronous network environment, driven by their massive scale, dynamic topologies, and significant delays. This unique complexity demands an adaptive packet routing algorithm that is asynchronous, risk-aware, and capable of balancing diverse and often conflicting QoS objectives in a decentralized manner. However, existing methods fail to address this need, as they typically rely on impractical synchronous decision-making and/or risk-oblivious approaches. To tackle this gap, we introduce PRIMAL, an event-driven multi-agent routing framework designed specifically to allow each satellite to act independently on its own event-driven timeline, while managing the risk of worst-case performance degradation via a principled primal-dual approach. This is achieved by enabling agents to learn the full cost distribution of the targeted QoS objectives and constrain tail-end risks. Extensive simulations on a LEO constellation with 1584 satellites validate its superiority in effectively optimizing latency and balancing load. Compared to a recent risk-oblivious baseline, it reduces queuing delay by over 70%, and achieves a nearly 12 ms end-to-end delay reduction in loaded scenarios. This is accomplished by resolving the core conflict between naive shortest-path finding and congestion avoidance, highlighting such autonomous risk-awareness as a key to robust routing.

Problem

• Develop asynchronous routing for dynamic LEO satellite networks
• Balance conflicting QoS objectives with risk-aware constraints
• Resolve conflicts between shortest-path routing and congestion avoidance

Innovation

• Event-driven multi-agent routing for independent satellite decisions
• Primal-dual approach managing worst-case performance degradation risks
• Learning full cost distribution to constrain tail-end QoS risks