Novel serial code concatenation strategies for error floor mitigation of low-density parity-check and turbo product codes
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a novel multiple serial code concatenation (SCC) strategy to combat the error-floor problem in iterated sparse graph-based error correcting codes such as turbo product-codes (TPC) and low-density parity-check (LDPC) codes. Although SCC has been widely used in the past to reduce the error-floor in iterative decoders, the main stumbling block for its practical application in high-speed communication systems has been the need for long and complex outer codes. Alternative, short outer block codes with interleaving have been shown to provide a good tradeoff between complexity and performance. Nevertheless, their application to next-generation high-speed communication systems is still a major challenge as a result of the careful design of long complex interleavers needed to meet the requirements of these applications. The SCC scheme proposed in this work is based on the use of short outer block codes. Departing from techniques used in previous proposals, the long outer code and interleaver are replaced by a simple block code combined with a novel encoding/decoding strategy. This allows the proposed SCC to provide a better tradeoff between performance and complexity than previous techniques. Several application examples showing the benefits of the proposed SCC are described. Particularly, a new coding scheme suitable for high-speed optical communication is introduced.
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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 it