Blind decision feedback equalizer based on high order MCMA
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Bibliographic record
Abstract
In this paper we propose a blind adaptive algorithm for the decision feedback equalizer based on high order MCMA (modified constant modulus algorithm) and decision directed adaptation. The originality of the proposed method is that the feedforward and the feedback filters are adjusted simultaneously by using the high order MCMA algorithm and by switching to the decision directed mode when the equalized symbols are in the convergence zones. This method reduces the propagation of error caused by the DFE structure. The mean square error (MSE) between the transmitted symbols and the equalized symbols is computed as performance metric. The proposed method is compared to the classic DFE based on second order MCMA. For all simulations, the ensemble-averaged MSE is obtained from 100 Monte Carlo runs. The obtained result show that the proposed method performs well for high constellations in the presence of Gaussian noise, comparatively with other methods.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it