LDPC-RS Product Codes for Digital Terrestrial Broadcasting Transmission System
Bibliographic record
Abstract
Product code is a promising technique for the next generation digital terrestrial broadcasting transmission system, due to its superior error correction performance. In this paper, we propose an Low Density Parity Check-Reed Solomon (LDPC-RS) product code structure, along with a novel hybrid iterative decoding scheme. The decoding scheme combines a hybrid LDPC decoding technique and RS+LDPC hard decision decoding to form a `turbo-like' decoding structure. By ingeniously ranging the order of different decoding techniques as well as performing error estimation and soft value modification at proper stages, the proposed decoding scheme greatly improves the error performance in low SNR regions while reducing the computational complexity in moderate and high SNR regions compared with the simple concatenation of LDPC and RS decoding. Moreover, we propose a rate compatible LDPC-RS product code to further reduce complexity by adaptively choosing the decoding code rate. Simulation results verify the outstanding error performance and suitability for the energy saving applications.
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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 | 0.000 |
| 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) | 0.001 | 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".