Analyzing Age Performance of Hybrid-ARQ: A Unified Explicit Result
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we offer an explicit, unified result that can generally depict the age performance of error-correcting techniques at the physical layer. We first propose a more realistic code-based status update system, wherein different types of delay elements, e.g., the coding delay, transmission delay, propagation delay, decoding delay and feedback delay are comprehensively considered. Under this system, we derive closed-form average Age of Information (AoI) expressions for reactive HARQ and proactive HARQ, respectively. On the basis of these explicit expressions, and utilizing the existing results for finite-length codes, we formulate an AoI minimization problem to investigate the age-optimal codeblock assignment strategy in the finite block-length (FBL) regime. Through case studies and analytical results, we provide comparative insights between reactive HARQ and proactive HARQ from the perspective of freshness of information. The numerical results and optimization solutions reveal that proactive HARQ draws its strength from both superior age performance and system robustness, thus enabling the potential to provide new system advancement for a freshness-critical status update system. The full paper version of this work is available on the arXiv at https://arxiv.org/abs/2204.01257.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it