Banding in optical add-drop multiplexers in WDM networks: preserving agility while minimizing cost
Bibliographic record
Abstract
In this paper, we examine the use of limited tunability in reconfigurable optical add-drop multiplexers (L-ROADM). L-ROADMs can add or drop from only a subset of adjacent wavelengths on the network and are less costly than fully-reconfigurable optical add-drop multiplexers (F-ROADMs). We quantify the trade-off between tuning range and the number of F-ROADMs that can be replaced by L-ROADMs without sacrificing the set of connections that can be established. For the limited-add and drop case, an analytical solution for the band size is found. For the limited-add or limited-drop case, a nearly linear relationship was found between the size of the band and the number of L-ROADMs required, and the number of additional wavelengths required never exceeded 20%. For example, if half of the nodes in the ring were equipped with L-ROADMs that operated on 50% of the total spectrum, full connectivity could still be achieved by employing as few as 7% extra wavelengths.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".