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 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".