On the performance of BICM with mapping diversity in hybrid ARQ
Bibliographic record
Abstract
Abstract Hybrid ARQ with packet combining for high‐order modulations (such as 16‐QAM) may be significantly enhanced if the bits‐to‐symbols mappings are appropriately changed throughout the transmissions. In this paper, we analyze the relationship between such mapping diversity and channel coding. We calculate the capacity of the popular bit‐interleaved‐coded modulation (BICM) to draw qualitative and approximate quantitative conclusions that are valid for strong codes approaching the capacity limits. We conclude that the choice/design of the appropriate mapping depends on the targeted spectral efficiency and we demonstrate that certain forms of mapping diversity may be counterproductive. We also show that iterative demapping may be successfully applied to significantly reduce (by more than 1 dB) the gap between the BICM and coded modulations (CM) capacities. The analysis is illustrated with results obtained when the mapping diversity is combined with practical turbo codes. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| 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".