Tight lower bounds on the symbol error rate of uncoded nonuniform signalling over AWGN channel
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Bibliographic record
Abstract
New Bonferroni-type lower bounds on the word error probability of uncoded systems are developed. The new family of bounds is based on a recent Bonferroni inequality proposed by Cohen and Merhav. These novel tight bounds are developed for optimal maximum a posteriori (MAP) coherent detectors with nonuniform signalling over additive white Gaussian noise channel. The results are compared to the state-of-the-art KAT lower bounds and it is shown that the superiority of one bound to another is dependent on the signal constellation, the amount of nonuniformity of the Bernoulli source to be communicated, and the SNR range of interest. For instance, for smaller deviations from the uniform case, which are in fact more plausible, and at low SNRs, the new bounds are tighter than KAT lower bounds for all the constellations studied.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it