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

Yuhua Zhao, Tiejun Lv, Ke Wang

TL;DR

This work introduces Cross-layer Age of Error Information (C-AoEI) to quantify the trade-off among data freshness, reliability, and transmission efficiency in satellite IoT systems employing L-HARQ with backtracking. It develops a joint encoding and adaptive retransmission framework that uses packet-level encoding and weight-based packet selection to balance freshness and efficiency, yielding a closed-form C-AoEI expression and an optimization strategy for decision thresholds and coding parameters. Simulation results show up to 31.8% gains in transmission efficiency and 17.2% reductions in C-AoEI, with robustness to inter-cell interference and dynamic channel conditions, illustrating practical benefits for latency-aware next-generation S-IoT protocols. The approach provides a principled, tractable method to design cross-layer, latency-sensitive satellite networking in large-scale multi-GBS deployments.

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.

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

TL;DR

This work introduces Cross-layer Age of Error Information (C-AoEI) to quantify the trade-off among data freshness, reliability, and transmission efficiency in satellite IoT systems employing L-HARQ with backtracking. It develops a joint encoding and adaptive retransmission framework that uses packet-level encoding and weight-based packet selection to balance freshness and efficiency, yielding a closed-form C-AoEI expression and an optimization strategy for decision thresholds and coding parameters. Simulation results show up to 31.8% gains in transmission efficiency and 17.2% reductions in C-AoEI, with robustness to inter-cell interference and dynamic channel conditions, illustrating practical benefits for latency-aware next-generation S-IoT protocols. The approach provides a principled, tractable method to design cross-layer, latency-sensitive satellite networking in large-scale multi-GBS deployments.

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.
Paper Structure (18 sections, 3 theorems, 74 equations, 13 figures, 4 tables, 1 algorithm)

This paper contains 18 sections, 3 theorems, 74 equations, 13 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Related Work
  4. System Model and Assumption
  5. System Model
  6. Channel Model
  7. Real-Time Processing Guarantee
  8. A Joint Encoding Packet and L-HARQ Information Delivery Scheme
  9. Adaptive Retransmission Policy
  10. Proposed Encoding Strategy
  11. C-AoEI in Proposed Retransmission Scheme
  12. C-AoEI-Parameter Relationship Analysis
  13. Explicit C-AoEI Expression
  14. Parameter Sensitivity Analysis
  15. Optimal Parameter Configuration
  16. ...and 3 more sections

Key Result

Theorem 1

The average C-AoEI of the considered S-IoT system is given by: where Under i.i.d. fading channels and constant packet-mixing rate, the closed-form expression simplifies to: where $P^{BT}_{lm}$ is the backtracking failure probability and $N_z$ is the number of undecodable packets.

Figures (13)

  • Figure 1: The system model for the satellite-terrestrial integrated networks.
  • Figure 2: The working flow of L-HARQ.
  • Figure 3: An illustration example of weighted coding strategy.
  • Figure 4: A packet example of weighted coding strategy.
  • Figure 5: The evolution of the instantaneous
  • ...and 8 more figures

Theorems & Definitions (3)

  • Theorem 1: Average C-AoEI for Truncated L-HARQ Systems
  • Theorem 2: Sensitivity of C-AoEI to Parameters
  • Theorem 3: Optimal Decay Factor