A novel radius-adjusted approach for blind adaptive equalization
Bibliographic record
Abstract
A new radius-adjusted approach for blind adaptive equalization for quadrature amplitude modulation (QAM) signals is introduced. Static circular contours are defined around an estimated symbol point in a QAM signal constellation, which creates regions that can be mapped to adaptation phases. The equalizer tap update consists of a linearly weighted sum of adaptation criteria that is scaled by a variable step size. Each region corresponds to a fixed step size and weighting factor, which creates a time-varying tap update based on the equalizer output radius. Two new algorithms are proposed based on this new approach and the multimodulus algorithm (MMA). The first algorithm trades off MMA and constellation-matched errors to reduce the time-to-convergence and mean-squared error (MSE), while the second trades off MMA and decision-directed errors to achieve reliable transfer between error modes and to obtain low MSE. A method to tune the proposed algorithms is developed based on statistics of the radius. The proposed algorithms are compared with related blind algorithms, and simulation results confirm that the proposed algorithms lead to enhanced performance.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".