Bibliographic record
Abstract
Optical switches and wavelength converters are recognized as two of the most important DWDM system components in future all-optical networks. Optical switches perform the key functions of flexible routing, reconfigurable optical cross-connect (OXC), network protection and restoration, etc. in optical networks. Wavelength Converters are used to shift one incoming wavelength to another outgoing wavelength when this needs to be done. Always residing in optical switches, they can effectively alleviate the blocking probability and help solve contention happening at the output port of switches. The deployment of wavelength converters within optical switches provides robust routing, switching and network management in optical layer, which is critical to the emerging all-optical Internet. However, the high cost of wavelength converters at current stage of manufacturing technology has to be taken into consideration when we design node architectures for an optical network. Our research explores the efficiency of wavelength converters in a long-haul optical network at different degrees of traffic load by running a simulation. Then, we propose a new cost-effective way to optimally design wavelength-convertible switch so as to achieve higher network performance while still keeping the total network cost down. Meanwhile, the routing and wavelength assignment (RWA) algorithm used in the research is designed to be a generic one for both large-scale and small-scale traffic. Removing the constraint on the traffic load makes the RWA more adaptive and robust. When this new RWA works in conjunction with a newly introduced concept of wavelength-convertible switches, we shall explore the impact of large-scale traffic on the role of wavelength converter so as to determine the method towards optimal use of wavelength convertible switches for all-optical networks.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".