Linearly Constrained Adaptive Echo Cancellation for Discrete Multitone Systems
Bibliographic record
Abstract
Achieving the full duplex transmission in digital subscriber lines (DSL) is possible by using echo cancellation methods. Most of the methods available for echo cancellation, in DSL systems based on the discrete multitone (DMT) modulation, deploy partial echo cancellation in the time and frequency domain. In these systems, the weights of the FIR filter emulating the echo in the time domain are mapped from the frequency-domain adaptive echo weights. This mapping can be regarded as a linear constraint on an extended weight vector containing weights in the time and frequency domain, and echo cancellation can then be regarded as a linearly constrained optimization on this extended linear space. In this paper, linearly constrained adaptive echo cancellation for DMT-based DSL systems is proposed. Two approaches are introduced based on the adaptive algorithms by Frost and by Griffiths. The proposed structure provides a unifying and flexible framework for echo cancellation in different DSL systems. Furthermore, in the proposed method the proper choice of the constraint can address the drifting problem in the presence of the narrow band noise in the DSL systems.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".