Dynamic WDM network performance: the impact of banding in reconfigurable optical add/drop multiplexers
Bibliographic record
Abstract
This thesis investigates the impact of banding in limited reconfigurable optical add/drop multiplexers (L-ROADMs) on network performance in dynamic wavelength division multiplexed (WDM) networks. We quantify the trade-off between L-ROADM tunability and the number of L-ROADMs in wavelength-routed, single-hop, single-fibre, unidirectional metropolitan ring networks. We find that L-ROADMs can be deployed under static traffic conditions without compromising network connectivity and without additional wavelengths. Under dynamic traffic conditions, 30% of the nodes can accommodate L-ROADMs under uniformly distributed traffic for an L-ROADM tuning range of 50% of the spectrum. This increases to 45% of the nodes for hub traffic. In both static and dynamic traffic, a hub network is most favourable for deploying L-ROADMs—a finding of particular relevance in metropolitan networks.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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 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".