Distributed Learning for Resilient LEO Satellite Access Under Adversarial Jamming

Abstract

Low-Earth-orbit (LEO) mega-constellations are expected to support ubiquitous connectivity, yet resilient access remains challenging under satellite mobility, adversarial jamming, and limited onboard resources. This paper investigates user association and scheduling in LEO networks under adversarial jamming, while accounting for link activation cost, finite radio frequency chains, and long-term queue stability requirements. We propose a decentralized online learning framework, termed Lyapunov-guided variance-aware multi-armed bandit (LV-MAB). Specifically, Lyapunov drift analysis converts the long-term queue stability requirement into a per-frame queue-weighted surrogate problem, which enables distributed user-side association and satellite-side scheduling. For user association, LV-MAB uses a variance-aware upper confidence bound method with link-level sliding windows and resets to track non-stationary rate statistics induced by mobility and dynamic jamming. For satellite-side scheduling, combinatorial Thompson sampling with posterior inflation is adopted to resolve contention among requesting users under radio frequency chain constraints. We establish dynamic regret bounds for LV-MAB and prove system queue stability. Extensive simulations show that LV-MAB achieves reliable throughput, backlog control, fairness improvement, and robustness under dynamic adversarial jamming.