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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".