ASTRA: Asynchronous Age-Aware Satellite Random Access via Mean-Field Control
Sayam Chakraborty, Aimin Li, Yigit Ince, Sajjad Baghaee, Elif Uysal

TL;DR
This paper introduces ASTRA, a mean-field control framework for asynchronous satellite IoT random access that optimizes freshness and reduces AoI by modeling complex interactions including capture and SIC.
Contribution
It develops a PHY-aware, asynchronous, mean-field model for satellite IoT access, capturing partial overlaps, capture, and SIC, with an age-threshold optimal policy.
Findings
Proposed policy reduces AoI compared to age-independent baselines.
Established existence of a mean-field equilibrium with age-threshold structure.
Model accurately captures asynchronous arrivals, partial overlaps, capture, and SIC.
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…
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