Event-Driven Collaborative Learning for Delay-Constrained Routing in LEO Satellite Networks

Abstract

Low Earth Orbit (LEO) constellations provide essential data transmission channels for ground-based edgecloud computing in 6G Space-Air-Ground Integrated Networks (SAGIN). Routing data for these delay-sensitive tasks requires inter-satellite routing to efficiently manage backhaul timeliness and limited onboard energy. However, dynamic topologies and queuing fluctuations cause severe delay uncertainty. Furthermore, state-space explosion in large-scale networks complicates energy optimization under strict delay constraints. To address these challenges, an event-driven learning framework for constrained distributed routing is proposed. First, routing is modeled as a constrained stochastic shortest path (C-SSP) problem with an absorbing terminal state via a constrained Markov Decision Process (CMDP) to minimize energy consumption under delay constraints. Second, an event-driven mechanism quantizes delay and resource slack into discrete events. This formulates a stochastic parameterized policy in a low-dimensional space, significantly reducing decision complexity. Finally, delay constraints are decoupled using Lagrangian relaxation, enabling a two-scale collaborative optimization. This alternates centralized multiplier updates with parallel distributed policy improvements, allowing individual nodes to execute decisions based solely on local observations and Monte Carlo estimations. Simulations demonstrate the method reduces energy consumption while guaranteeing expected delays.