Blind Bit-Rate Detectors for variable-gain multiple-access systems in unknown Gaussian channel
Bibliographic record
Abstract
We propose a robust maximum a posteriori probability (MAP) blind bit-rate detector (BBRD) for a fixed frame-length multiple access system which employs variable-gain receiver power and repetition encoding. this detector considers the rate detection (RD) as a multihypothesis test and maximizes the likelihood functions (LF)s to find the true bit-rate. Assuming that we have no knowledge about the receiver gain and the noise variance and using the maximum likelihood (ML) estimates of the unknown parameters in the LFs, the resulting GLR test fails, since a possible transmitted sequence of one hypothesis is also a possible transmitted sequence for another hypothesis. To overcome this problem, we propose a hybrid likelihood ratio test (HLRT) by assuming the information sequence as independent uniformly distributed random variables, averaging the LFs over them and then substitution of the ML estimates of the receiver gain and the noise variance in the resulting LFs. In addition, we propose a quasi-HLR detector, that substitutes the ML estimates of the unknown gain and noise variance from the original pdf in the resulting LFs after averaging over the information sequence. Simulation results compare the performances of the new BBRDs.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".