Analysis of a Power Line Communication System Over a Non-white Additive Gaussian Noise Channel and Performance Improvement Using Diversity Reception
Bibliographic record
Abstract
Performance improvement of a power line communication system is presented here considering the noise as a cyclostationary non-white Gaussian random process. Performance of a power line communication system is severely deteriorated by fading and multipath effect. The impulsive noise has been considered time variant, has short duration, random occurrence with a high power spectral density (PSD). It causes bit error in the signal. Using orthogonal frequency division multiplexing (OFDM) technique, the effect of impulsive noise and fading can be improved greatly. An analytic approach is presented to evaluate the performance of a power line communication link in the presence of the above limitations. The simulation results show that there is deterioration in system BER due to time and frequency dependence of noise and the degradation is found to be significant at higher bit rates and bandwidth. The system suffers penalty in receiver sensitivity due to non-white nature of the noise process. In this paper, an analytical approach using diversity reception is carried out to examine the performance improvement of a power line channel in fading and impulsive noise. The system bit error rate (BER) is compared numerically for both binary phase shift keying (BPSK) and OFDM system. The BER results show that there is significant improvement in OFDM. Also the performance is remarkably upgraded using diversity reception.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".