Efficient erasure marking technique for delay reduction in DSL systems impaired by impulse noise
Bibliographic record
Abstract
Digital Subscriber Line (DSL) technologies have experienced rapid development. Protecting the DSL systems against Impulse Noise (IN) is an important issue and it has recently received considerable attention. A combination of Reed–Solomon (RS) codes and interleaving is used to mitigate the destructive effects of IN. However, it is shown that the interleaving structure introduces long delay, which is certainly undesirable in high-rate transmission systems supporting interactive applications such as Internet Protocol Television (IPTV). Different techniques have therefore been proposed to reduce the interleaving delay while still being able to effectively protect the systems from IN. In particular, Error and Erasure Decoding (EED) can be used instead of Error Decoding (ED) to improve the decoder correction capability, which in turn helps reducing the required interleaving depth and delay. To fully explore the error correction capacity of the EED, reliable erasure marking becomes essential. This thesis proposes an erasure marking technique that fully explores the correction capacity of the EED, and correspondingly, facilitates a shorter interleaving. We first study the sources that generate impulse noise and the statistics of impulse noise in DSL systems. Analytical models for the distribution of amplitude and inter-arrival time of impulse noise are also provided. Based on the statistics of impulse noise, a squared-distance based erasure marking technique is then proposed. Furthermore, analysis of selecting proper parameters for the proposed technique is developed. Finally, the Peak Signal-to-Noise Ratio (PSNR) performance of IPTV over DSL in presence of IN is investigated with the proposed erasure marking technique employed.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".