Estimation and decoding strategies for channels with abruptly changing statistics
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes iterative estimation and decoding techniques for memoryless channels with a bounded number of abrupt changes in channel statistics. Specifically, the channel under consideration is a binary symmetric channel with a crossover probability that changes a bounded number of times during the transmission of a codeword; the channel state information to be estimated consists of the crossover probabilities of the different segments and the location(s) of the transition point(s). To estimate the transition points, a technique developed for source coding of piecewise-stationary memoryless sources is adapted; then the expectation-maximization algorithm is used to estimate the crossover probabilities. This segmentation/estimation is carried out on the error sequence of the currently hypothesized frame. Simulation results using turbo codes indicate that the proposed receiver performs almost as well as a receiver that has perfect knowledge of the channel.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it