Bibliographic record
Abstract
Data transmission over power lines is an attractive alternative to well-established wireline and wireless communication technologies. One of the main challenges in accomplishing reliable power-line communication (PLC) is channel impairment through electromagnetic interferences, or so-called impulse noise. In this paper, we consider transmission over impulse-noise channels for a typical narrowband system architecture employing convolutional codes and Viterbi decoding. We present different decoding metrics, including new designs adopted from the multiuser detection literature, and we derive expressions for cutoff rate and bit-error rate (BER) performances of the corresponding decoders. These expressions are amenable for quick numerical evaluation and thus, constitute a valuable tool for decoder optimization and performance comparison. Our numerical and BER simulation results show that one of the proposed metrics enables robust decoding without knowledge of the statistic of the impulse noise with a performance close to that of optimum decoding, which relies on the noise statistic. It is further highlighted that, different from transmission over the Gaussian-noise channel, quadrature detection is beneficial in case of real-valued modulation and passband transmission over impulse-noise channels.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".