Maximum a posteriori bit-rate detectors for variable-gain multiple-access systems in unknown gaussian channel
Bibliographic record
Abstract
We propose a MAP blind bit-rate detector for a fixed frame-length multiple-access system which employs variable-gain transmitter power and repetition encoding. This detector considers the problem as a multi-hypothesis testing problem and maximizes the likelihood functions to find the true bit-rate. In contrast of the existing literature, the proposed bit-rate detector assumes no knowledge about the transmitter gain and the noise variance of the system and provides an efficient implementable closed form solution using the maximum likelihood (ML) estimates of the unknown parameters in the likelihood functions (LFs). Assuming the information sequence also as unknown deterministic values and substituting their ML estimates, a GLRT-based detector is obtained. However, this detector fails since a possible transmitted sequence of one hypothesis is also a possible transmitted sequence for all next hypotheses. To overcome this problem, we assume the information sequence as samples of an independent uniformly distributed random variable and average the likelihood functions (after substitution of the ML estimates of the transmitter gain and the noise variance) over the information sequence. The implementation of the resulting hybrid LRT (HLRT) is computationally complex. Therefore, we propose a suboptimal solution with substantially less complexity and compare its performance with the existing bit-rate detector obtained assuming known parameters
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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 | 0.006 |
| 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 | 0.001 |
| 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".