Advanced Signal Processing for Digital Subscriber Lines
Bibliographic record
Abstract
The recent deployment of digital subscriber line (DSL) technology around the world is rapidly making broadband access for the mass consumer market a reality. The ever-growing customer demand for higher data rates has been fueled by the popularity of applications like peer-to-peer (P2P) filesharing networks and video-streaming and high-definition television (HDTV). DSL technology allows telephone operators to getmaximum leverage out of their existing infrastructure by delivering broadband access over existing twisted-pair telephone lines. At the heart of DSL lies a plethora of signal processing techniques which enable such high-speed transmission to be achieved over a medium originally designed with only voice-band transmission in mind. These advanced signal processing techniques address many challenges that exist in DSL networks today, such as the near-end and far-end crosstalk (NEXT/FEXT), impulse noise, peak-toaverage-power ratio (PAR), intersymbol and intercarrier interference (ISI/ICI), radio-frequency interference (RFI), and so forth. The goal of this special issue is to discuss the state-of-the-art and recent advances in signal processing techniques for DSL.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.013 |
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".