Improving a Power Line Communications Standard with LDPC Codes
Bibliographic record
Abstract
We investigate a power line communications (PLC) scheme that could be used to enhance the HomePlug 1.0 standard, specifically its ROBO mode which provides modest throughput for the worst case PLC channel. The scheme is based on using a low-density parity-check (LDPC) code, in lieu of the concatenated Reed-Solomon and convolutional codes in ROBO mode. The PLC channel is modeled with multipath fading and Middleton's class A noise. Clipping is introduced to mitigate the effect of impulsive noise. A simple and effective method is devised to estimate the variance of the clipped noise for LDPC decoding. Simulation results show that the proposed scheme outperforms the HomePlug 1.0 ROBO mode and has lower computational complexity. The proposed scheme also dispenses with the repetition of information bits in ROBO mode to gain time diversity, resulting in 4-fold increase in physical layer throughput.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".