A novel radius-adjusted approach for blind adaptive equalization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 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