All-optical swapping of spectral amplitude code labels for packet-switched networks
Bibliographic record
Abstract
Packet-switched networks have attracted considerable attention as a basis for next-generation optical networks due to their advantages in terms of flexibility and network efficiency over traditional circuit-switched networks. Optical code multi-protocol label switching (OC-MPLS) promises fast, flexible, power-efficient switching by keeping signals in the optical domain and avoiding costly conversions to the electrical domain. In this paper, we review the use of spectral amplitude codes (SACs) for implementing OC-MPLS labels. We discuss the principles and features, as well as key enabling technologies required for their processing. In particular, we compare three different approaches for low cost all-optical swapping of SAC labels. All approaches are based on semiconductor fiber lasers and exploit nonlinearity in a semiconductor device: the first uses cross-absorption modulation in an electroabsorption modulator, the second uses cross-gain modulation (XGM) in a semiconductor optical amplifier (SOA), and the third makes use of XGM in an SOA as well as injection locking in a Fabry-Pérot laser. We present the static and dynamic responses of each for swapping a multi-wavelength input label to a multi-wavelength output label. The benefits and limitations of each approach as well as future improvements are discussed. We also present the results of systems experiments which demonstrate error-free all-optical label swapping, recognition, and switching of multi-rate packets in packet-switched networks using multi-wavelength labels.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".