A Novel Hybrid ARQ Scheme Based on LDPC Code Extension and Feedback
Bibliographic record
Abstract
The design of an efficient hybrid automatic repeat request (ARQ) scheme based on rate compatible low density parity check (LDPC) codes is considered. It has been shown that extending as well as puncturing of LDPC codes can produce good rate compatible LDPC codes for the additive white Gaussian noise channel. One issue with the traditional LDPC-based hybrid ARQ methods is that the throughput drops off significantly at low signal-to-noise ratios (SNRs). In this paper we introduce a coding scheme which is capable of using puncturing, extending and feedback at the same time to address this issue. Appropriate choice of feedback functions along with optimum combining of received signals for the belief propagation decoder mitigates the throughput drop- off issue at low SNRs, while having a small feedback overhead from the receiver. A powerful mother code is generated via the progressive edge growth algorithm and is used for puncturing and extending in the proposed scheme. Clustering the codewords of the longest codebook is used to decrease the overhead of the feedback connection. Simulation analysis of the throughput shows that our scheme could get as close as 0.5 dB to the Shannon limit while having up to 2 dB gain compared to previous works at low SNRs.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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".