A new reliability-based incremental redundancy hybrid ARQ scheme using LDPC codes
Bibliographic record
Abstract
We present a new reliability-based hybrid automatic repeat request (RB-HARQ) scheme based on low density parity check (LDPC) codes. With the proposed RB-HARQ, which uses a rate-compatible LDPC code with puncturing and extending, the longest codeword is divided into clusters of code bits. Unlike previous works, in the event of a decoding failure, the receiver measures the reliability of received clusters, instead of code bits, and determines which cluster would be most beneficial for retransmission. Several metrics to determine the best cluster candidates for retransmission are derived analytically. We show that one of the new metrics outperforms the previous metrics. We also show that even with the feedback overhead taken into account, our RB-HARQ can still result in significant gain over the previous works, provided that the cluster size is appropriately selected.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".