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Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

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Published on: March 20, 2017

Enhancing generalization in neural network-based waveform-level channel modeling for optical fiber transmission

Minghui Shi, Hang Yang, Chuyan Zeng

|November 11, 2025

Summary

This summary is machine-generated.

A new parameter encoding structure for neural networks (NNs) significantly enhances optical fiber channel waveform modeling accuracy. This approach enables a single NN to generalize across multiple system parameters for improved optical communication system design.

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Last Updated: Jan 11, 2026

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Area of Science:

Optical Communications
Computational Photonics
Machine Learning in Engineering

Background:

Accurate optical fiber channel waveform modeling is critical for optical communication systems.
Traditional methods like split-step Fourier method (SSFM) are computationally intensive.
Neural network (NN) approaches offer reduced computational load with comparable accuracy.

Purpose of the Study:

To improve the generalization capability of NNs for optical communication system modeling.
To develop a novel parameter encoding structure for enhanced NN performance.
To create a single NN capable of generalizing across diverse system parameters.

Main Methods:

Introduction of a novel parameter encoding structure for NNs.
Pre-encoding of system parameters to enhance NN generalization.
Training and validation of a single NN across multiple optical system parameters.

Main Results:

The parameter encoding structure significantly improves NN generalization.
Waveform modeling accuracy increased by 49.9% and 69.7% in generalized scenarios.
A single NN demonstrated generalization across modulation format, symbol rate, WDM channel space, laser parameters, dispersion, span length, and distance.

Conclusions:

The proposed parameter encoding structure substantially enhances NN generalization for optical waveform modeling.
A single, generalized NN was developed for the first time, covering multiple system parameters simultaneously.
This enhanced NN offers significant potential for optimizing optical transmission system design.