Correlation Between Peak-to-Average Power Ratio and Four-Wave Mixing in Optical OFDM Systems
Bibliographic record
Abstract
Coherent optical orthogonal frequency-division multiplexed (OFDM) systems have been shown to be vulnerable to fiber nonlinearity, four-wave mixing (FWM) in particular. Consequently, transmission of OFDM symbols with a low peak-to-average power ratio (PAR) is widely recommended. In this paper, we examine the correlation between PAR and the intensity of light generated through FWM for two different systems. It is demonstrated that the two quantities are strongly correlated in the region of high PAR values and, furthermore, OFDM symbols with very large PAR are likely to induce unusually strong FWM noise. On the other hand, the correlation is less pronounced for moderate PAR values, especially in the presence of a large amount of uncompensated fiber dispersion. Selective mapping (SLM), a simple coding method to avoid high-PAR symbols, is then introduced and its impact on the overall FWM generation process is analyzed. It is shown that such a reshaping of the PAR distribution eliminates undesirable bursts of FWM-induced noise, enhancing the overall system tolerance to fiber nonlinearity at a very small sacrifice in data rate. A concatenation of SLM with a previously proposed nonlinearity compensation scheme is also investigated. It is demonstrated that the benefits provided by the two methods can be easily combined, leading to further performance improvements.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".