Bibliographic record
Abstract
We experimentally demonstrate, for the first time to our knowledge, simultaneous all-optical 2R regeneration of multiple WDM channels. In the recent work [1], our group has proposed an all-optical 2R regeneration scheme capable of handling multiple WDM channels simultaneously. Our proposed multi-channel regenerator is a modified configuration of Mamyshev's 2R regenerator [2], in which a conventional highly-nonlinear-fiber (HNLF) is replaced by a novel dispersion-managed nonlinear medium. The proposed multi-channel regeneration scheme uses multiple concatenated dispersion-managed sections, where each dispersion-managed section contains a piece of HNLF and a periodic-group-delay device (PGDD). For the proof-of-principle demonstration of the regenerator we have built a recirculating loop, where, instead of cascading multiple identical HNLF-PGDD sections, we use only one such section and pass the signal through it multiple times. In this dissertation, we present our experimental results on single- and multi-channel all-optical 2R regeneration. We experimentally demonstrate single-channel 2R regeneration in a dispersion-managed configuration of Mamyshev's regenerator. The experimentally observed 3dB eye-opening improvement confirms that single-channel performance is not degraded by dispersion management. The multi-channel regeneration experiments were performed with as many as 12 channels (12 x 10 Gb/s), and as few as 2 channels (2 x 10 Gb/s). We discuss our experimental results on 2-, 8-, and 12-channel all-optical regeneration. All 12 channels demonstrate eye-opening improvement better than 2 dB. We have experimentally characterized the performance of our regenerator with respect to the number of neighboring channels. Our experimental results show that `unlike the prior attempts of multi-channel all-optical regeneration by other groups' our dispersion management technique overcomes the regenerator degradations by inter-channel four-wave mixing (FWM) and cross-phase modulation (CPM).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 teacher head, 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".