ASTRA: Asynchronous Age-Aware Satellite Random Access via Mean-Field Control

Abstract

Satellite Internet-of-Things (IoT) enables massive status-update services beyond terrestrial coverage, but grant-free uplink access creates a coupled freshness-control problem: increasing repetition and receiver-side diversity improves a device's capture-SIC opportunities, yet the resulting population congestion degrades network-wide freshness. Existing AoI-aware random-access models often rely on slot-synchronous collisions, fixed delivery probabilities, or scalar transmit-or-wait decisions and therefore cannot capture asynchronous satellite uplinks with capture and SIC. This paper develops a PHY-aware mean-field framework, termed ASTRA (Asynchronous Age-Aware Satellite Random Access), for freshness-driven satellite IoT random access. We build an access model that captures asynchronous arrivals, partial overlaps, capture, and SIC while preserving the dependence of delivery success on each device's repetition-diversity action. We then formulate the population interaction as a scalable mean-field MDP in which devices optimize access timing and intensity using only local AoI observations. The resulting system admits a mean-field equilibrium in which individual optimality and endogenous congestion are mutually consistent. We further prove that the optimal equilibrium policy admits an age-threshold structure. Numerical results show that the proposed policy reduces AoI relative to age-independent baselines.