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

2026-05-18

AI Summary

This work addresses the coupled conflict between information freshness, measured by Age of Information (AoI), and retransmission diversity arising from asynchronous grant-free uplink access in satellite Internet-of-Things networks. The paper proposes a physical-layer-aware random access framework that, for the first time, jointly models asynchronous packet arrivals, partial signal overlap, capture effect, and successive interference cancellation (SIC). Leveraging a mean-field Markov decision process based solely on local AoI observations, an optimal equilibrium policy with an age-threshold structure is derived, aligning individual optimality with system-wide congestion control. Both theoretical analysis and simulations demonstrate that the proposed policy significantly reduces the network-average AoI compared to age-agnostic baseline approaches.

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.

Problem

• Age of Information
• Satellite IoT
• Random Access
• Asynchronous Uplink
• Network Freshness

Innovation

• mean-field control
• Age of Information (AoI)
• asynchronous random access
• satellite IoT
• capture and SIC