Bit-Interleaved Coded Modulation with Mismatched Decoding Metrics
Bibliographic record
Abstract
Bit-interleaved coded modulation (BICM) has become the de facto coding standard for communication systems. Recently, BICM has been cast as a mismatched decoding scheme due to the assumption of independent bit metrics. In addition to this inherent mismatch, practical demodulators may produce mismatched decoding metrics because of implementation constraints, such as clipping and metric approximation to reduce computational complexity. In this paper, we investigate BICM with such metrics. In line with recent works on this topic, we adopt the generalized mutual information (GMI) as the pertinent performance measure. First, we show that level-dependent scaling of logarithmic bit metrics can improve the BICM GMI. Second, we propose a uniform metric scaling which can lead to an improved performance of mismatched sum-product symbol-by-symbol decoding, even if the GMI is not changed. Third, we investigate general metric-mismatch correction methods and analyze their effects in terms of the GMI. By means of three application examples, we illustrate that metric-mismatch correction, including metric scaling, can significantly increase BICM rates.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".