Experimental Verification of Multilevel Coded Modulation for 16-Ary Constellations
Bibliographic record
Abstract
We present an experimental demonstration of multilevel coded modulation (MLCM) for 16-ary constellations. Performance of MLCM coding is compared for two 16 QAM constellations: 1) a phase noise optimized and 2) a square constellation. We quantify experimentally the phase noise regimes where the optimized constellation outperforms square 16 QAM. While MLCM coding is designed for one linewidth at a given symbol time, for the same overhead we show it outperforms uniform rate coding over a wide range of linewidths. The MLCM coding strategy minimizes block error rate under some simplifying assumptions. We show that under realistic conditions, the MLCM also gives good post forward error correction and quantify the advantage over uniform rate coding experimentally.
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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 | 0.000 | 0.000 |
| 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".