Bibliographic record
Abstract
Raptor codes and rate-compatible low-density parity-check (RC-LDPC) codes have drawn much attention in recent years as they can approach channel capacity without requiring channel information at the transmitter. Raptor codes have been shown to uniformly approach the binary-input AWGN channel capacity, especially at low SNR's, whereas RC-LDPC codes have the potential to provide higher throughput than Raptor codes at high SNR's. In this paper, we use different message word sizes to compare the throughput of three rateless codes, namely, Raptor codes, rate-compatible irregular repeat-accumulate (RC-IRA) codes, and the rate-compatible quasi-cyclic LDPC (RC/QC-LDPC) codes proposed in the 3GPP2 and 802.20 standards. The comparison is focused on short message word lengths under 16-symbol quadrature amplitude modulation (16-QAM). The simulation results in the AWGN channel show that RC-IRA and RC/QC-LDPC codes outperform Raptor codes at high SNR's. Under frequency flat Rayleigh fading channels, RC-IRA codes outperform RC/QC-LDPC codes at high SNR's and perform slightly worse at low SNR's. We also show that for short block lengths, the throughput of RC-IRA codes is not particularly sensitive to the mother code rate, the belief propagation (BP) algorithm scheduling, the existence of parallel edges during check node combining, and the symbol degree distribution (for fixed average left degree).
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".