A reduced complexity multi-rate echo canceller for dmt based DSL systems
Bibliographic record
Abstract
In the past years, a number of researchers have investigated the use of digital echo cancellation systems for transmission technologies based on discrete multi-tone (DMT) modulation such as used in asymmetrical digital subscriber lines (ADSL) and the likes. This paper reports on a novel set of techniques that reduces the computational complexity of the multi-rate echo canceller structure at the lower transmit rate side of the link, e.g. the remote terminal (RT) side in ADSL. In this case, the echo canceller located in the terminal processes the signals into the entire frequency bandwidth (higher rate). In fact, the echo channel estimated during the processing corresponds to the imperfect hybrid channel combined with the low pass filtering action of the analog-to-digital converter (ADC). In such a situation, we can exploit the special structure of the echo channel to reduce the computational complexity of the echo cancellation processing. Specifically, this is achieved by zeroing the high frequency coefficients of the echo channel estimate. The frequency representation of the echo allows the exploitation of certain properties of the inverse fast Fourier transform (IFFT) algorithm to further reduce the complexity. Experimental simulations for an ADSL like configuration show a computational complexity reduction factor of as much as 67% without significantly affecting the residual echo level
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".