Improving HomePlug Power Line Communications with LDPC Coded OFDM
Bibliographic record
Abstract
Power line communications (PLC) has received much attention due to the wide connectivity and availability of power lines. Effective PLC must overcome the harsh and noisy environments inherent in PLC channels. HomePlug 1.0 is the current PLC standard in North America. The physical layer of HomePlug 1.0 employs orthogonal frequency division multiplexing (OFDM) as well as concatenated Reed-Solomon and convolutional coding. Aiming to obtain higher PLC throughput, we investigate the performance of OFDM with low-density parity-check (LDPC) codes and compare the proposed scheme with HomePlug 1.0 ROBO mode. In our simulations, the PLC channel is modeled by multipath fading, with Middleton's Class A noise (AWCN) model simulating the worst-case impulsive noise. We apply clipping to lessen the impact of impulsive noise. A simple but effective method is devised to estimate the variance of the clipped noise for LDPC decoding. In comparison with ROBO mode, the proposed scheme offers improved performance and lower computational complexity per decoded bit. Our scheme provides increased throughput by dispensing with ROBO mode's repetitive transmission of information to gain time diversity
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| 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".