Performance analysis of a multi‐hop power line communication system over log‐normal fading in presence of impulsive noise
Bibliographic record
Abstract
The authors present a study on the end‐to‐end average bit error rate (BER), the average channel capacity and the outage performance of a multi‐hop power line communication (PLC) system equipped with decode‐and‐forward (DF) relays. To combat the issue of distance dependent signal attenuation, multi‐hop data transmission has recently been introduced for PLC systems. However, apart from the distance dependent signal attenuation, PLC systems also suffer from (i) the variation in signal amplitude (fading) because of reflections and (ii) impulsive noise. Thus, in this study, the channel for each hop of the multi‐hop PLC system is modelled by a log‐normal fading amplitude, which is clubbed to a distance dependent signal attenuation factor. To consider the effect of the impulsive noise along with the background noise, the additive noise at each node is modelled by a Bernoulli–Gaussian process. Analytical expressions for the end‐to‐end average BER for binary phase‐shift keying, the average channel capacity and the outage probability are obtained. The merit of the multi‐hop PLC system over a conventional direct transmission PLC system for fixed transmission power is shown through numerical results. The authors' results show that with increasing number of DF relays, the end‐to‐end average BER, the average channel capacity and the outage performance improve.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".