Comparison of two nonuniformly-spaced decision feedback equalizers for sparse multipath channels
Bibliographic record
Abstract
Performance comparisons between the conventional decision feedback equalizer (DFE) and an alternative design, known as the decision directed feedback equalizer (DDFE), are conducted with nonuniform spacings on sparse multipath channels. The optimum feedforward filters (FFF) of the DDFE and its variant under the minimum mean square error (MMSE) criterion are derived with the constraint of a nonuniformly spaced feedback filter (NU-FBF), and relationships regarding the optimum tap values and the resultant MMSE between FFF with uniform and nonuniform spacings are established. While the bit error rate (BER) results obtained confirm previous claims that the T-spaced NU-DDFE exhibit prominent improvement over the T-spaced NU-DFE in equalizing long sparse channels such as those encountered in high definition television (HDTV) systems, the gain is only minimal, if any, when applied on simpler sparse channels with fewer multipath terms. Moreover, using a T/2-spaced FFF in the NU-DDFE demands a T/2-spaced FBF to mitigate additional intersymbol interference (ISI) caused by the T/2-spaced channel samples, and a better method than the commonly-used thresholding scheme may be required to accurately determine the tap positions of the T/2-spaced NU-FBF.
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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 | 0.001 | 0.004 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".