A General Framework for Mixed-Domain Echo Cancellation in Discrete Multitone Systems
Bibliographic record
Abstract
In full-duplex communication systems with discrete multi-tone (DMT) modulation, echo cancellers are employed to cancel echo by means of adaptive filters. Generally, the structure present in the DMT signals is used to decrease the computational complexity of these cancellers by splitting the operations between the time and frequency domains. In this work, we introduce a general framework for designing echo cancellers for such systems in an arbitrary mixed domain. This is achieved by introducing a generic decomposition of the Toeplitz data matrix at the transmitter in terms of arbitrary unitary matrices. Then, based on this decomposition, a new mixed-domain echo cancellation structure is derived, which performs an exact instantaneous gradient-type adaptation. This mixed-domain configuration is also extended for realizing constrained adaptation whereby linear constraints are used to ensure the proper mapping of the weight vectors in different domains. The proposed structures offer a unified framework to study existing cancellers and to design new ones with better performance measures. This framework is employed to propose a new canceller based on discrete trigonometric transformations. The analytical and numerical results presented show that this canceller has a faster convergence rate than the existing ones with similar complexity and is more robust.
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 | 0.000 |
| 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 | 0.001 |
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".