An Analysis of a Near-End Crosstalk Cancelation System That Uses Adaptive Filters
Bibliographic record
Abstract
An analysis of a near-end crosstalk (NEXT) cancelation system that uses adaptive digital filters is described. The analysis is based on two well-known models for the NEXT coupling factor, the Bradley and Lin models, and yields the minimum number of adaptive filters required to reduce the NEXT below a prescribed level to within a defined confidence factor. With the minimum number of adaptive filters known, the required computational resources for the application at hand can be estimated. The analysis is further extended to practical situations where the largest NEXT signals chosen for elimination are incorrectly detected, and estimates of the minimum and maximum increase in the uncanceled NEXT due to incorrect detection are then deduced. Simulations show that the estimated minimum number of adaptive filters required and the maximum and minimum increase in uncanceled NEXT due to incorrect detection are fairly close to corresponding estimates obtained on the basis of measurements for both the Bradley and the Lin models. Therefore, by using the proposed analysis the minimum number of adaptive filters can be deduced without the need for time-consuming and expensive experiments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".