Constrained Adaptive Echo Cancellation for Discrete Multitone Systems
Bibliographic record
Abstract
In communication systems where full-duplex transmission is required, echo cancellers are deployed to cancel the interference of the transmitted signal at the collocated receiver. For systems using discrete multitone (DMT) modulation, echo cancellation is performed partially in the time and frequency domains to decrease the processing complexity. In this paper, echo cancellation for DMT systems is reformulated as a constrained optimization problem, where a cost function is minimized over an extended linear space. This extended space contains the weights of the finite-impulse-response (FIR) filter emulating the echo channel in the time and the frequency domains, while linear constraints are used to ensure the proper mapping between these two domains. Based on this proposed formulation, a new constrained adaptive echo cancellation structure for DMT-based digital subscriber lines (DSL) systems is proposed. The proposed formulation provides a unifying framework for different practical DSL systems (i.e., frame asynchronous and multirate), as well as additional flexibility in implementation by allowing the incorporation of supplementary constraints that can improve the performance of the system. As an illustrative example, we show how the robustness of the echo canceller can be improved in the presence of radio frequency interference by adding appropriate constraints on the extended linear space.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".