Novel Method for Multi-Dimensional Mapping of Higher Order Modulations\n for BICM-ID Over Rayleigh Fading Channels
Bibliographic record
Abstract
Multi-dimensional (MD) mapping offers more flexibility in mapping design for\nbit-interleaved coded modulation with iterative decoding (BICM-ID) and\npotentially improves the bandwidth efficiency. However, for higher order signal\nconstellations, finding suitable MD mappings is a very complicated task due to\nthe large number of possible mappings. In this paper, a novel mapping method is\nintroduced to construct efficient MD mappings to improve the error performance\nof BICM-ID over Rayleigh fading\n channels. We propose to break the MD mapping design problem into four\ndistinct $2$-D mapping functions. The $2$-D mappings are designed such that the\nresulting MD mapping improves the BICM-ID error performance at low signal to\nnoise ratios (SNRs). We also develop cost functions that can be optimized to\nimprove the error performance at high SNRs. The proposed mapping method is very\nsimple compared to well-known mapping methods, and it can achieve suitable MD\nmappings for different modulations including higher order modulations for\nBICM-ID. Simulation results show that our mappings significantly outperform the\npreviously known mappings at a target bit error rate (BER) of $10^{-6}$. Our\nmappings also offer a lower error-floor compared to their well-known\ncounterparts.\n
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".