Adaptive Hybrid ARQ Systems With BCJR Decoding
Bibliographic record
Abstract
We propose and evaluate a novel method of constructing Hybrid Automatic Repeat reQuest (HARQ) systems using the specific properties of the Bahl, Cocke, Jelinek, and Raviv (BCJR) error-correcting algorithm. Because the convergence to the actual codeword is not always guaranteed with the BCJR approach, we propose a system in which two different types of Negative AcKnowledgement messages (NAKs) are employed. The first type is the conventional 1-bit NAK, and the second type specifies retransmission pattern in such a way that the additional parity bits are concentrated on the parts of the code trellis that did not converge to a valid sequence. This is different from the traditional construction of rate-compatible punctured codes (RCPCs), which attempts to obtain the optimal weight distance properties of the codes without taking the convergence properties of the BCJR decoder into account. We demonstrate the performance of the algorithm using RCPCs, and we show that our system outperforms the best known conventional HARQ scheme in terms of the throughput and the average length of retransmitted blocks on practical Gaussian, Rayleigh, and thresholded Rayleigh channels. Moreover, as opposed to other adaptive HARQ algorithms, our solution requires no precomputed lookup tables, and it is robust to changes in the channel characteristics and only introduces moderate increase in feedback link throughput requirements.
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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 | 0.002 | 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".