Adaptive Equalization of A Communication Channel-in A Non-Gaussian Noise Environment
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Bibliographic record
Abstract
The subject of adaptive filters constitutes an important part of statistical signal processing. Adaptive filters are successfully applied in such diverse fields as communications, control, radar, sonar, and biomedical engineering. In this paper we study the use of the particle filter for adaptive equalization of a linear dispersive channel that produces (unknown) distortion. The performance of the adaptive filter is compared to that of least-mean-square (LMS) and recursive-least-square (RLS) algorithms. The main advantage of the particle filter when compared to other algorithms is its robustness when dealing with non-Gaussian noise. The particle filter showed better performance in convergence speed and root-mean-square (rms) error in case of low signal-to-noise ratio.
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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 | 0.000 |
| Open science | 0.001 | 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