A 9.52 dB NCG FEC Scheme and 162 b/Cycle Low-Complexity Product Decoder Architecture
Bibliographic record
Abstract
Powerful forward error correction (FEC) schemes are used in optical communications to achieve bit-error rates (BERs) below 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-15</sup> . These FECs follow one of two approaches: the concatenation of simpler hard-decision codes or the usage of inherently powerful soft-decision codes. The first approach yields lower net coding gains (NCGs), but can usually work at higher code rates and have lower complexity decoders. In this paper, we propose a novel FEC scheme based on a product code and a post-processing technique. It can achieve an NCG of 9.52 dB at a BER of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-15</sup> and 9.96 dB at a BER of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-18</sup> , an error-correction performance that sits between that of current hard-decision and soft-decision FECs. A decoder architecture is designed, tested on field programmable gate array and synthesized in 65-nm CMOS technology: its 162 b/cycle worst-case information throughput can reach 100 Gb/s at the achieved frequency of 616 MHz. Its complexity is shown to be lower than that of hard-decision decoders in literature, and an order of magnitude lower than the estimated complexity of soft-decision decoders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".