NUC Optimization Design for Multi-layer Layered Division Multiplexing
Bibliographic record
Abstract
Layered Division Multiplexing (LDM) is a powerbased non-orthogonal multiplexing (NOM) technology used in Advanced Television System Committee (ATSC) 3.0 next generation TV standard. To improve the performance of the multi-layer LDM system, this paper proposes a low-complexity scheme that optimizes the injection level and non-uniform constellation (NUC) to approach the capacity. The computable integral form of the bit interleaved coded modulation (BICM) capacity is derived for the multi-layer LDM. The injection levels are first optimized with the help of the BICM capacity formula. The NUCs are optimized layer by layer from the lower layer to the upper layer based on the optimized injection levels to maximize the BICM capacity. Simulation results show that the injection levels and NUCs obtained by the proposed scheme perform excellently and provide as much as 0.8 dB gain for the middle layers in the LDM system, compared with ATSC 3.0.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".