Cohen–Merhav bounds on the symbol error rate of uncoded signalling in AWGN interference
Bibliographic record
Abstract
Abstract Using a recent Bonferroni‐type inequality proposed by Cohen and Merhav, we develop new tight lower bounds on the word error probability of uncoded systems with optimal Maximum A Posteriori (MAP) coherent detection for non‐uniform signalling over additive white Gaussian noise channel. Our results are compared to the state‐of‐the‐art Kuai‐Alajaji‐Takahara (KAT) lower bounds and it is shown that the superiority of one bound to another is dependent on the signal constellation, the amount of non‐uniformity of the Bernoulli source to be communicated and the SNR range of interest. It is noted that bounding techniques for the performance evaluation of communication systems are receiving increasing attention today, thanks to their suitability for a wide variety of schemes ranging from uncoded signalling to space–time‐coded multiple‐input multiple‐output (MIMO) systems. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.014 | 0.065 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".