Filter Design for SOA-Assisted SS-WDM Systems Using Parallel Multicanonical Monte Carlo
Bibliographic record
Abstract
We address design and optimization of optical filters for spectrum-sliced wavelength division multiplexed (SS-WDM) systems employing saturated semiconductor optical amplifiers (SOAs) to suppress intensity noise. We study the impact of the shape of both slicing and channel selecting optical filters vis-a-vis two important impairments: the filtering effect and the crosstalk. The quantification of bit error rate (BER) is made possible by a parallel implementation of the multicanonical Monte Carlo algorithm. The intensity noise suppression by the SOA and signal degradation by subsequent optical filtering are studied both numerically and experimentally. We find optical filter shape and bandwidth that minimizes BER. By varying channel spacing and width, we estimate the achievable spectral efficiency when using both noise-cleaning SOA and forward error correction. We show that when constrained to use a symmetric architecture, i.e., identical filters for both slicing and channel selecting filters, there is a degradation in achievable spectral efficiency. We show that noise suppression is robust to variations in relative channel powers in multichannel systems. Our numerical simulations, vetted experimentally, provide accurate and quantitative results on optimized system performance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".