BERT Chart Analysis of Adaptive and Non-Adaptive Turbo Frequency Domain Equalization
Why this work is in the frame
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Bibliographic record
Abstract
This paper analyzes the performance of turbo frequency domain equalization (TFDE) with 16-QAM by bit error rate transfer (BERT) chart. We derive the BERT chart for the two cases in which the equalizer adapts or does not adapt from iteration to iteration. In the first case, the equalizer parameters change during iterations based on the feedback information and in the latter case they do not change. In the BERT chart analysis of TFDE, we theoretically find the mean and variance of the Gaussian-like distribution of the equalizer output. The mean and variance of the equalizer output depend on the channel parameters, channel signal to noise ratio and the decoder bit error rate in the previous iteration. Finally we compare the BERT chart results with simulation results and show that the predicted results are fairly accurate.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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