Performance analysis of a power line communication system employing selection combining in correlated log‐normal channels and impulsive noise
Bibliographic record
Abstract
The authors analyse an L ‐channel selection combining (SC) scheme for a power line communication (PLC) system with binary phase‐shift keying. The focus is on improving the reliability in data transfer of the system instead of improving the data rate. To enhance the reliability in data transfer, multiple PLC channels are used to send the same information‐bearing signal to the receiver. The L PLC channels are subject to log‐normal fading, which is modelled by a multivariate log‐normal distribution with an exponential correlation. The channels are also corrupted by additive impulsive noise as well as thermal noise. To consider the effect of both types of noises, they adopt a Gaussian mixture noise model, in which the additive noise samples are taken from a Bernoulli–Gaussian process. The system performance is evaluated in terms of the average bit error rate and the average channel capacity, for which approximate closed form expressions are derived. Numerical results showing the impact of the number of PLC channels, the amount of correlation, the noise scenarios, and the fading environments on the performance are presented. The authors' results show that the performance improves with increasing number of PLC channels; however, the amount of improvement reduces with increasing channel correlation.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".