Optical GMPLS networks with code switch capable layer for sub-wavelength switching
Bibliographic record
Abstract
We propose a novel extension to the label mapping space in GMPLS networks by exploiting further physical layer properties. The proposed extension, OC-GMPLS (optical code enabled GMPLS), is a modified version of the standard GMPLS. OC-GMPLS relies on adding an optical CDMA code switching layer called code switch capable (CSC) layer to the existing label mapping layers. OC-GMPLS provides a larger label mapping space and enhances the QoS capabilities for optical networks through increasing the granularity of traffic identification. In our extension, we use Manchester signaling to allow for more robust clock recovery while providing enhanced performance over conventional on-off keying (OOK) schemes. We also provide a reference architecture for the label switching layers in our OC-GMPLS architecture capable of performing labelling through optical CDMA. We analyze the performance of the proposed scheme and show that our proposal significantly enhances the overall network performance.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".