A New Proposed Model for Dispersion Compensation via Linear Chirped Fiber Bragg Grating
Bibliographic record
Abstract
In recent years, the technology of fiber optics communication has experienced immense evolution due to the huge data rates and transmission capacity requirements of emerging communication systems.However, nonlinear effects and dispersion which are challenging impairments are among the main issues that have an impact on the optical fiber systems performance.As a result, to solve this challenging issue, the utilization of a dispersion compensating technique such as Dispersion Compensating Fiber (DCF) or Fiber Bragg Grating (FBG) is an urgent necessity.In this paper, a new model for dispersion compensation utilizing Fiber Bragg Grating is proposed.Tanh apodization and linear chirp functions are added to the proposed model to provide a Linear Chirped FBG.In addition, it has been used with a Gaussian filter of low pass type to enhance the proposed system in terms of performance parameters such as: BER, Q-factor, and eye-diagram.These parameters are the most significant performance measurements that impact any optical communication system.The proposed model is simulated via Optisystem and analyzed via Python.Simulation results for different lengths of fiber show that the BER, Q-factor, and eye diagrams of the proposed model have a better performance compared to that of the other systems where either no FBG is applied or a traditional uniform FBG is used.The Q-factor values and BER values for 40km fiber length were 29.5524 and 9.3407*10 -182 for the system model proposed in this study, 13.9969 and 6.5823*10 -45 for the system with uniform FBG, and 5.0736 and 1.1792*10 -7 for the system without FBG.Finally, the eye diagrams of the proposed model also show a better opening compared to other systems.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".