Optimizing Age of Information in Polar-Coded Status Update System
Bibliographic record
Abstract
Age of information (AoI) defines the freshness of status update in real-time systems, such as the Industrial Internet of Things (IIoT), and can be affected by delays and transmission error probability. To improve the reliability of data transmissions, the recent AoI works on physical layer considered applying practical coding schemes. Since polar codes can be strictly proved to achieve the channel capacity, this article makes an effort to comprehensively investigate and optimize the AoI performance in a polar-coded status update system. First, we propose a practical code-based status update system that takes full consideration of encoding, transmission, propagation, decoding, and feedback delays in AoI analysis. Then, we analyze and derive the average AoI of the proposed system with various transmission protocols. The simulation results of a polar-coded system validate the theoretical analysis and show that hybrid automatic repeat request (HARQ) achieves better AoI performance than non-HARQ. To optimize AoI in polar-coded status update system, we further improve the designs for HARQ with chase combining (HARQ-CC) and HARQ with incremental redundancy (HARQ-IR), respectively. The design signal-to-noise ratio (SNR), puncturing length of HARQ-CC are optimized by traversal, while the code lengths for each transmission and maximum transmission times of HARQ-IR are optimized by the greedy algorithm. Simulation results show that the proposed HARQ can achieve better average AoI performance than traditional HARQ.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".