Interferometric noise in optical add/drop multiplexers based on fiber Bragg gratings
Bibliographic record
Abstract
The role of wavelength division multiplexing (WDM) in telecommunication networks can be expanded well beyond providing high capacity on point-to-point transmission links. WDM can be used to perform network functions such as routing, switching and add/drop multiplexing. The optical add/drop multiplexer (OADM) is a key component for WDM networks. An OADM adds and drops one or more of the signals in a wavelength division multiplex of optical signals without interfering with other channels on the fiber. Different approaches are available for implementing OADMs. These include thin film filters, arrayed waveguide gratings, circulators with fiber Bragg gratings (FBGs), and FBGs in the arms of a Mach-Zehnder interferometer (MZI). Fig. 1 shows configurations of add/drop multiplexers that use fiber Bragg gratings with two different resonant wavelengths'. One configuration is based on an MZI and FBGs, and the other is based on circulators and FBGs. In order to add/drop more than one channel, a multiple number of narrowband gratings are used. Imperfect reflection of the FBGs at the resonant wavelengths will introduce multipath interference from the reflections at discrete points. This is referred to as interferometric noise. 201 and 202 are the selected wavelengths for add/drop and correspond to the resonant wavelengths of the gratings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".