Quasi-Cyclic Spatially Coupled LDPC Code for Broadcasting
Bibliographic record
Abstract
Flexible and robust transmission schemes are required to support different coded modulation modes, receiver types and channel conditions in future broadcasting systems. However, conventional low-density parity-check (LDPC) coded schemes are usually optimized towards particular scenario, and may face the problem of performance degradation in other different scenarios. In this paper, a type of quasi-cyclic spatially coupled LDPC (QC-SC-LDPC) codes, which have been recently proven to be able to universally achieve capacity over different channels under conventional belief propagation decoding, are proposed as the forward error correcting codes for broadcasting applications. Then a QC-SC-LDPC coded transmission scheme is proposed, which is capable of showing good performance in various scenarios under practical constraints of finite coupling length, smoothing parameter, uncoupled code length and number of decoding iterations. To maintain the universal property under the above constraints, a typical construction method is developed, based on which a QC-SC-LDPC code is constructed as an example. Finally, the performance and robustness of the proposed QC-SC-LDPC coded transmission scheme are demonstrated via simulations under different yet typical coded modulation modes and channel conditions.
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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 | 0.001 |
| 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) | 0.001 | 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".