Age-Optimal Network Coding HARQ Scheme for Satellite-Based Internet of Things
Bibliographic record
Abstract
Satellite-based Internet of Things (S-IoT) is viewed as an efficient solution to provide timely status updates to the terrestrial user equipment (UE), due to its ubiquitous coverage and broadband access capability inherited from high throughput satellite (HTS). However, the conventional hybrid automatic repeat request (HARQ) cannot guarantee the freshness of status update transmission, because the reliable transmission needs the retransmission of the lost packets, which deteriorates the freshness due to the nontrivial propagation delay and high bit error rate (BER) of the satellite–territory link (STL). In this article, we propose an age-optimal network coding HARQ (NC HARQ) scheme with the metric of information timeliness, i.e., Age of Information (AoI) to realize timely status updates in S-IoT. First, we model the STL as a shadowed Rician (SR) fading channel and derive the closed-form expressions of BER. Then, we propose a fixed interval NC inserted HARQ (f-NC HARQ) scheme, where the NC packets are inserted in the information packets with fixed interval to accelerate the recovery of lost information packets and derive the expressions of Peak AoI (PAoI) and average end-to-end delay. Furthermore, we propose an adaptive NC inserted HARQ (A-NC HARQ) scheme for the drastic variations in the SR fading channel, where the transmission of the status update is modeled as a partially observable Markov decision process (POMDP) problem and solved by a low complexity improved fast informed bound (iFIB) algorithm. Simulation results validate the accuracy of our theoretical derivations and show that the A-NC HARQ scheme can achieve the lowest PAoI and average end-to-end delay.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".