UVL-based hybrid neural network model for generalized optical fiber channel modeling

Area of Science:

• Optical Communications Engineering
• Computational Photonics
• Artificial Intelligence in Communications

Background:

• Accurate modeling of fiber channels is crucial for optical communication systems but traditional methods like the split-step Fourier method (SSFM) are computationally intensive.
• Existing neural network (NN) approaches, including Transformer and bidirectional long short-term memory (Bi-LSTM), accelerate modeling but struggle with complexity and speed for multi-channel transmissions.
• NNs require strong generalization capabilities to handle variations in system parameters like channel count, distance, and launch power.

Purpose of the Study:

• To develop a universal virtual lab (UVL)-based hybrid neural network (NN) scheme for accurate and generalized modeling of multi-channel optical fiber transmissions.
• To leverage fast multi-channel nonlinear interference noise (NLIN) calculation and the fitting capabilities of NNs to overcome the limitations of conventional methods.
• To significantly improve the efficiency and flexibility of modeling for complex optical communication systems.

Main Methods:

• Proposed a hybrid neural network modeling scheme integrated with a universal virtual lab (UVL).
• Utilized fast multi-channel nonlinear interference noise (NLIN) calculation capabilities.
• Employed the fitting and generalization abilities of neural networks (NNs) for modeling.
• Validated the model against split-step Fourier method (SSFM) benchmarks for wavelength-division multiplexing (WDM) transmissions.

Main Results:

• The proposed hybrid NN model achieves accurate modeling for multi-channel transmissions, maintaining signal-to-noise ratio (SNR) errors within 0.5 dB of SSFM benchmarks.
• Demonstrated a significant speedup of over 550× for an 800-km wavelength-division multiplexing (WDM) transmission with 17 channels compared to conventional methods.
• The model exhibits strong generalization ability, accommodating variations in system parameters.

Conclusions:

• The UVL-based hybrid neural network scheme offers a highly efficient and accurate solution for modeling multi-channel optical fiber communication systems.
• This approach substantially enhances modeling flexibility and computational speed, addressing the limitations of traditional time-consuming methods.
• The developed model is suitable for evaluating complex optical communication systems with varying parameters and multiple channels.