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

Technical Summary: C-AoEI-Aware Cross-Layer Optimization in Satellite IoT Systems

Problem Statement
Satellite-based Internet of Things (S-IoT) systems face a fundamental performance trilemma: long propagation delays, dynamic channel fading (typically modeled as shadowed-Rician), and stringent bandwidth scarcity. While Layer-coded Hybrid Automatic Repeat Request (L-HARQ) enhances reliability by utilizing backtracking decoding to recover sequences of previously failed packets, it introduces a critical ambiguity in data freshness metrics. Standard Age of Information (AoI) metrics fail in this context because they do not account for the temporal discrepancy between feedforward decoding (immediate) and backtracking decoding (delayed recovery of older packets). Consequently, conventional AoI cannot accurately quantify the trade-off between data freshness and transmission efficiency, often leading to suboptimal configurations where throughput optimization inadvertently maximizes information age. Furthermore, existing solutions relying on increasing parallel HARQ processes degrade efficiency under extended round-trip times (RTT) and stop-wait protocols.

Methodology
To address these limitations, the authors propose a novel cross-layer optimization framework centered on a new metric: Cross-layer Age of Error Information (C-AoEI).

1. System Architecture: The study models a satellite-terrestrial integrated network serving multiple Ground Base Stations (GBSs) over shadowed-Rician fading channels. The system employs a truncated L-HARQ scheme where packets are encoded into sub-packets. Upon a feedforward decoding failure, the receiver utilizes backtracking decoding, leveraging prior information from mixed packets to recover previous data.
2. C-AoEI Metric Derivation: Unlike traditional AoI, C-AoEI is defined as the time elapsed since the last successful backtracking decoding status update. The authors derive a closed-form expression for C-AoEI (Theorem 1), explicitly linking freshness degradation to system parameters, including the inter-departure time between successful feedforward decodings ($Y_m$) and the backtracking decoding depth ($B_m$).
3. Adaptive Encoding Strategy: A packet-level encoded L-HARQ scheme is developed for multi-GBS scenarios. The authors introduce an adaptive algorithm that jointly optimizes decision thresholds and encoding parameters. Specifically, a tunable decay factor ($\beta$) governs the selection probability of unacknowledged packets for retransmission. This allows the system to dynamically balance the trade-off between transmission efficiency (minimizing redundant retransmissions) and data freshness (prioritizing newer updates).
4. Optimization: The framework formulates an optimization problem to minimize C-AoEI while maintaining transmission efficiency. Theoretical analysis provides the sensitivity of C-AoEI to parameters like the packet selection weight ($\omega_i$) and the decay factor ($\beta$), identifying optimal configurations for different operational regimes (freshness-priority vs. efficiency-priority).

Key Contributions

• C-AoEI Metric: The proposal of a new metric that resolves the temporal ambiguity inherent in L-HARQ systems by explicitly incorporating backtracking decoding constraints into freshness quantification.
• Closed-Form Analysis: The derivation of a closed-form expression for average C-AoEI, which characterizes the relationship between information freshness, channel dynamics, and decoding depth without relying on iterative simulations.
• Adaptive Cross-Layer Framework: The design of a joint encoding and decision-threshold optimization algorithm that dynamically adjusts to channel conditions and interference levels (including inter-cell interference from multiple GBSs).
• Robust Architecture: A system model that concurrently handles inter-satellite communications and GBS connectivity, specifically analyzing the impact of path loss exponents and power disparity ratios on system performance.

Simulation Results
Extensive simulations validate the proposed framework against conventional schemes:

• Performance Gains: The proposed framework achieves a 31.8% higher transmission efficiency and a 17.2% lower C-AoEI compared to traditional L-HARQ schemes.
• Robustness: The system demonstrates resilience against inter-cell interference and varying channel conditions. For instance, as the path-loss exponent increases (simulating a transition from free space to dense urban environments), the proposed method limits C-AoEI degradation to 37.4%, whereas conventional schemes suffer a 54.2% deterioration.
• Parameter Sensitivity: The results confirm that tuning the decay factor $\beta$ allows for a flexible trade-off. Increasing $\beta$ in specific regimes can reduce C-AoEI while maintaining efficiency, effectively mitigating the "freshness-reliability" conflict.
• Latency: The worst-case execution time (WCET) for preprocessing operations (CSI estimation, packet selection, parameter adaptation) is calculated at 0.30 ms, which is significantly faster than the LEO propagation delay (3.3 ms), ensuring real-time readiness.

Significance
The paper claims that this work bridges a critical theoretical gap by providing the first analysis of the impact of satellite channel environments on AoI under L-HARQ schemes with network encoding. By establishing an explicit analytical connection between freshness degradation and channel dynamics, the proposed C-AoEI framework provides a foundation for designing efficient, latency-aware protocols for next-generation S-IoT. The framework is particularly relevant for mission-critical applications such as maritime emergency monitoring, wildfire tracking, and infrastructure inspection in extreme environments, where the trade-off between data freshness and transmission reliability directly impacts operational outcomes.