OOK Q-factor degradation in scalable optical switches
Bibliographic record
Abstract
All-optical switching has been proposed to overcome the limitations of electronic switches in terms of scalability, speed, footprint, and power consumption. A key passive optical component to bypass electronic processing limitations is the arrayed waveguide grating (AWG). Switch architectures combining wavelength converters and fiber delay lines provide tunable routing and contention resolution when used with AWGs. An AWG passively routes either single or multiple input port wavelengths to its output ports. A single wavelength per port strategy reduces crosstalk within the AWG, but drastically increases the dimensionality of the device. Physical constraints on AWG design limit the port number for the foreseeable future to under 100. To scale optical switches to emerging network requirements, we can use multiple wavelengths per port. In this paper we examine one multiple wavelength per port architecture and quantify the physical layer impairments due not only to the AWG crosstalk, but also Q-factor degradation due to multiple wavelength conversions, and as a function of the number of recirculations in the contention resolution delay lines. While previous work has addressed this issue in terms of accumulated loss, we focus on accumulated relative intensity noise and amplified spontaneous emission.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".