C-AoEI-Aware Cross-Layer Optimization in Satellite IoT Systems: Balancing Data Freshness and Transmission Efficiency

TL;DR

This paper introduces a novel cross-layer optimization framework for satellite IoT systems that balances data freshness and transmission efficiency using a new metric, C-AoEI, and demonstrates significant performance improvements through simulations.

Contribution

It proposes the C-AoEI metric and a joint optimization scheme for coding and decision thresholds in satellite IoT, addressing the limitations of existing AoI measures.

Findings

Achieves 31.8% higher transmission efficiency

Reduces C-AoEI by 17.2%

Robust against interference and channel variations

Abstract

Satellite-based Internet of Things (S-IoT) faces a fundamental trilemma: propagation delay, dynamic fading, and bandwidth scarcity. While Layer-coded Hybrid ARQ (L-HARQ) enhances reliability, its backtracking decoding introduces age ambiguity, undermining the standard Age of Information (AoI) metric and obscuring the critical trade-off between data freshness and transmission efficiency. To bridge this gap, we propose a novel cross-layer optimization framework centered on a new metric, the Cross-layer Age of Error Information (C-AoEI). We derive a closed-form expression for C-AoEI, explicitly linking freshness to system parameters, establishing an explicit analytical connection between freshness degradation and channel dynamics. Building on this, we develop a packet-level encoded L-HARQ scheme for multi-GBS scenarios and an adaptive algorithm that jointly optimizes coding and decision thresholds. Extensive simulations demonstrate the effectiveness of our proposed framework: it achieves 31.8% higher transmission efficiency and 17.2% lower C-AoEI than conventional schemes. The framework also proves robust against inter-cell interference and varying channel conditions, providing a foundation for designing efficient, latency-aware next-generation S-IoT protocols.