Towards Age-Optimal Transmission in Satellite-Integrated IoT: A Two-Layer Coding Approach
Bibliographic record
Abstract
To support the emergent freshness-critical applications in the satellite-integrated IoT, information must be transmitted timely and reliably. A significant limitation of the upcoming satellite-integrated IoT era is the non-trivial propagation latency because of long-distance communication. To realize timely information delivery, the hybrid automatic repeat request (HARQ) strategy with frequent feedback is not fit anymore, since the reliability of the HARQ strategy needs multiple retransmission of the obsolete packets, which inevitably result in information staleness in the satellite-integrated IoT. In this paper, we design a two-layer coding strategy that uses error-correction codes within each packet in the physical-layer (PHY) and erasure-correction codes across the packets in the packet-layer. Then, we formulate an AoI-optimal redundancy-allocation problem to find the redundancy compromise between error-correction codes and erasure-correction codes. By solving the redundancy-allocation problem for the designed two-layer coding strategy, we derive explicit expressions of the AoI-optimal two-layer coding rates. Inspired by this, we explore the optimal reliability of the physical channel. Numerical results and analysis prove that making the physical channel suitably unreliable is beneficial to the timeliness of the system. And the simulation results indicate that the combination of erasure-correction codes and relative lossy error-correction codes achieves AoI-improvement over the ultra-reliable PHY-only coding scheme. The simulation results also show that the choice of AoI-optimal coding rates depends heavily on the channel characteristics, such as the signal-to-noise ratio, fade duration and channel fading parameters.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".