Low Complexity Hybrid ARQ Using Extended Turbo Product Codes Self-Detection
Bibliographic record
Abstract
This paper presents a hybrid automatic repeat request (HARQ) system using a parity error checking (PEC) technique with low processing power requirements. The proposed technique is applied to extended turbo product codes (TPC) where the parity check bits used for extending the component codes of TPC, are exploited to replace the conventional cyclic redundancy check (CRC) error detection in HARQ systems. Consequently, the required processing power can be reduced substantially while the throughput is almost unchanged for long TPC codes, or increased for short TPC codes. The proposed PEC technique is also compared to the state-of-the-art syndrome error checking (SEC) as well as conventional CRC. Monte Carlo simulation results reveal that PEC- HARQ can provide equivalent throughput to SEC-HARQ and higher throughput than CR-HARQ systems. Moreover, numerical results show that the PEC technique has lower computational complexity than both SEC and CRC error detection. In particular cases, the complexity of the proposed system is reduced by more than 50% as compared to the state- of-the-art, and by more than 80% when compared to the CRC error detection.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".