Synthetic Optical Network Dataset with Q-Factor, BER, and Receiver Sensitivity Metrics under EDFA-FBG Conditions

This dataset presents a large-scale synthetic simulation of performance parameters in fiber-optic communication systems, specifically designed to evaluate the impact of EDFA (Erbium-Doped Fiber Amplifier) and FBG (Fiber Bragg Grating) components under varying transmission distances and conditions. The dataset comprises 1,000,000 rows of simulated records, each capturing key optical network metrics including Q-Factor, Bit Error Rate (BER), and Receiver Sensitivity for both downstream and upstream transmission directions.

The core objective of this dataset is to provide a controlled and reproducible framework for studying how signal quality degrades or improves under different fiber distances and the presence or absence of EDFA/FBG devices. The dataset generation process was carefully crafted using stochastic modeling based on empirical trends observed in real-world optical experiments. The following parameters are included in the dataset:

- Distance_km: The length of the optical fiber in kilometers. Values include typical transmission spans (0, 10, 20, 40, 45, 70, and 75 km).
- Q_Factor_Downstream and Q_Factor_Upstream: Quality factor metrics for downstream and upstream channels respectively. These values are adjusted based on the transmission distance and the presence of EDFA/FBG to reflect signal integrity.
- BER_Downstream and BER_Upstream: Bit Error Rates computed from the Q-factor using an exponential decay model that mimics realistic signal degradation in optical fibers.
- Receiver_Sensitivity_Downstream and Receiver_Sensitivity_Upstream: Values representing how sensitive the receiver is to signal quality degradation at different distances, influenced by the transmission condition.
- Power_Level_dBm: Simulated variation in power levels (in dBm) to account for fluctuations due to hardware or environmental conditions.
-mNoise_Factor: A synthetic noise parameter that introduces random variations to simulate physical imperfections and disturbances in signal transmission.
Condition: Indicates whether the data point is simulated under "No_EDFA_FBG" or "With_EDFA_FBG" conditions.

The dataset serves as a valuable resource for research in several domains including:

- Optical Network Simulation and Modeling
- Machine Learning for Optical Systems
- Performance Prediction and Optimization in Fiber-Optic Networks
- Benchmarking of AI-based Diagnostic Tools in Telecommunications

Researchers can use this dataset to train, validate, and benchmark machine learning models for signal classification, fault detection, adaptive modulation, or link quality prediction. The inclusion of both amplified and unamplified scenarios makes the dataset versatile for comparative studies and ablation analysis.

This synthetic dataset is fully reproducible, extendable, and free from real-world acquisition constraints, making it suitable for academic and industrial experimentation, prototyping, and algorithm development in next-generation optical communication systems.