Low-Complexity Zipper-LDPC and Low-Latency Zipper-BCH Concatenated Codes
Bibliographic record
Abstract
A novel concatenated forward error correction (FEC) scheme is proposed that consists of an inner zipper framework with low-density parity-check component codes and an outer zipper framework with BCH component codes. The proposed soft-decision inner codes have a universal, flexible, and implementation-friendly structure with low decoding complexity. The inner code is error-reducing, tasked with reducing the error rate on bits passed to the outer hard decision code below its threshold. The proposed outer codes have ultra-low overhead and are designed to have small decoding memory and latency. Computer simulations show that the proposed zipper-BCH codes can reduce the decoding latency and required memory by up to a factor of 30, compared to the conventional zipper codes, while maintaining a minimal performance loss. The proposed scheme is considered with higher-order modulation with both multi-level coding and bit-interleaved coded modulation. Simulations show that the concatenated FEC scheme can provide a 0.1 dB gain over the state-of-the-art FEC designs, making it a highly attractive FEC solution for high-throughput optical communication systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".