Optimal Signaling Scheme and Capacity Limit of PLC Under Bernoulli-Gaussian Impulsive Noise
Bibliographic record
Abstract
This paper studies the optimal signaling scheme and capacity of the Bernoulli-Gaussian impulsive noise channel to shed new light on the impact of impulsive noise on spectral efficiency of power-line communications systems. First, by focusing on the practically typical case with impulse power that is much higher than signal power, we develop a tight approximation to the differential entropy of Bernoulli-Gaussian noise. Closed-form tight lower and upper bounds on the capacity are then derived. By comparing these bounds, it is demonstrated that the capacity decreases with an increasing impulse occurrence rate and the Gaussian signaling scheme is nearly optimal. We then focus on the case with an impulse power lower than signal power to develop tight lower and upper bounds. We subsequently show that the Gaussian signaling can approach the capacity in this region as well. In addition, channel erasure is shown to be very effective for the impulsive noise channel when impulse power is higher than signal power, but it introduces rate loss when impulse power is sufficiently lower than signal power. Illustrative simulation results confirm the analytical derivations and show their applications to estimate the maximum achievable rate of a power-line communication link with practical parameters.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".