Efficient Implementation of Low-Density Parity-Check Convolutional Code Encoders With Built-In Termination
Bibliographic record
Abstract
Low-density parity-check convolutional codes (LDPC-CCs) have demonstrated comparable error-correcting performance to LDPC block codes (LDPC-BCs). However, the LDPC-CC encoder requires termination when applied to finite-length data frames to ensure that the trailing information bits are fully protected. In this paper, the LDPC-CC encoder design is investigated, and a novel termination scheme is proposed. Starting from any encoder state, the proposed scheme is capable of generating a termination sequence in hardware without padding, thus minimizing the rate loss due to termination. A high-speed architecture for LDPC-CC encoders with built-in termination is proposed. Synthesis results for LDPC-CCs of code memory size up to 512 demonstrate maximum encoding throughputs of around 1 Gb/s for a 90-nm CMOS technology. The implementation cost for such encoders is shown to be reasonably low for average-sized LDPC-CCs.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".