Longitudinal Performance Monitoring Towards Hollow Core Fiber Systems via Node Nonlinearity
Bibliographic record
Abstract
The advent of hollow core fibers (HCF) in optical communication systems presents both opportunities and challenges for established monitoring techniques due to their ultra-low nonlinearity compared to traditional solid core fibers (SCF). Longitudinal performance monitoring (LPM) is an effective diagnostic method that leverages nonlinear effects to detect and localize impairments such as polarization-dependent loss, multipath interference, and differential group delay (DGD). This work explores the possibility of LPM in HCF systems that incorporate SCF segments. Through both simulation and experimental demonstrations, we show that the nonlinearity inherent in the SCF segments within the link enables continued LPM operation by producing detectable signals. Using commercial transceivers, we validate monitoring of both longitudinal power profile and distributed DGD with HCF-emulating spans. The results confirm that the transmission impairments can still be monitored and localized in HCF systems by exploiting SCF nonlinearities. Our experimental findings demonstrate the adaptability and robustness of LPM for next-generation optical networks based on HCF technology.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".