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Related Experiment Videos

A Hybrid Deep Learning Approach for Performance Prediction in Optical Communication Systems Based on PON Scenarios.

Ali Muslim1, Esra Gündoğan2, Mehmet Kaya1

  • 1Department of Computer Engineering, Fırat University, Elazığ 23119, Türkiye.

Sensors (Basel, Switzerland)
|May 4, 2026
PubMed
Summary

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This summary is machine-generated.

A hybrid deep learning framework accurately predicts performance in next-generation optical access networks. This approach offers a faster, more adaptable alternative to traditional methods for evaluating transmission in high-capacity passive optical networks (PONs).

Area of Science:

  • Optical Communications
  • Machine Learning
  • Network Performance Analysis

Background:

  • Optical access networks are evolving towards higher capacity and complexity.
  • Traditional physics-based models struggle with nonlinear and stochastic behaviors in modern passive optical networks (PONs).
  • Accurate performance prediction is crucial for next-generation optical networks.

Purpose of the Study:

  • To propose a hybrid deep learning (DL) framework for predicting key performance indicators (KPIs) in asymmetric 160/80 Gbps TWDM-PON systems.
  • To enhance the robustness and predictive accuracy of performance evaluation methods.
  • To provide a computationally efficient alternative to conventional optical simulation.

Main Methods:

  • Developed a hybrid DL framework integrating Gradient Boosting Regression and Multi-Layer Perceptron models.
Keywords:
MAEMSETWDM-PONdeep learningoptical access networkpassive optical network (PON)

Related Experiment Videos

  • Utilized an ensemble learning structure for improved prediction.
  • Generated a synthetic dataset of 1000 samples simulating diverse transmission scenarios (distance, power, noise).

    • Main Results:

    • Achieved strong agreement between DL-based predictions and conventional optical simulation outcomes.
    • Demonstrated superior adaptability and reduced computational complexity compared to traditional methods.
    • Obtained high coefficients of determination (R² > 0.94) and low error metrics, confirming framework effectiveness.

    Conclusions:

    • The proposed hybrid DL framework is a fast and reliable tool for performance prediction in next-generation optical access networks.
    • This approach effectively captures complex network behaviors, outperforming traditional methods.
    • Highlights the potential of DL for optimizing future optical network design and operation.